Journal of Information Processing Systems

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A Survey of Face Recognition Techniques

  • Published : 2009.06.30
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Abstract

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

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References

  1. A. K. Jain, R. Bolle, and S. Pankanti, 'Biometrics: Personal Identification in Networked Security,' A. K. Jain, R. Bolle, and S. Pankanti, Eds.: Kluwer Academic Publishers, 1999
  2. K. Kim, 'Intelligent Immigration Control System by Using Passport Recognition and Face Verification,' in International Symposium on Neural Networks. Chongqing, China, 2005, pp.147-156
  3. J. N. K. Liu, M. Wang, and B. Feng, 'iBotGuard: an Internet-based intelligent robot security system using invariant face recognition against intruder,' IEEE Transactions on Systems Man And Cybernetics Part C-Applications And Reviews, Vol.35, pp.97-105, 2005 https://doi.org/10.1109/TSMCC.2004.840051
  4. H. Moon, 'Biometrics Person Authentication Using Projection-Based Face Recognition System in Verification Scenario,' in International Conference on Bioinformatics and its Applications. Hong Kong, China, 2004, pp.207-213
  5. D. McCullagh, 'Call It Super Bowl Face Scan 1,' in Wired Magazine, 2001
  6. CNN, 'Education School face scanner to search for sex offenders.' Phoenix, Arizona: The Associated Press, 2003
  7. P. J. Phillips, H. Moon, P. J. Rauss, and S. A. Rizvi, 'The FERET Evaluation Methodology for Face Recognition Algorithms,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, pp.1090-1104, 2000 https://doi.org/10.1109/34.879790
  8. T. Choudhry, B. Clarkson, T. Jebara, and A. Pentland, 'Multimodal person recognition using unconstrained audio and video,' in Proceedings, International Conference on Audio and Video-Based Person Authentication, 1999, pp.176-181
  9. S. L. Wijaya, M. Savvides, and B. V. K. V. Kumar, 'Illumination-tolerant face verification of low-bitrate JPEG2000 wavelet images with advanced correlation filters for handheld devices,' Applied Optics, Vol.44, pp.655-665, 2005 https://doi.org/10.1364/AO.44.000655
  10. E. Acosta, L. Torres, A. Albiol, and E. J. Delp, 'An automatic face detection and recognition system for video indexing applications,' in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol.4. Orlando, Florida, 2002, pp.3644-3647
  11. J.-H. Lee and W.-Y. Kim, 'Video Summarization and Retrieval System Using Face Recognition and MPEG-7 Descriptors,' in Image and Video Retrieval, Vol.3115, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2004, pp.179-188 https://doi.org/10.1007/b98923
  12. C. G. Tredoux, Y. Rosenthal, L. d. Costa, and D. Nunez, 'Face reconstruction using a configural, eigenface-based composite system,' in 3rd Biennial Meeting of the Society for Applied Research in Memory and Cognition (SARMAC). Boulder, Colorado, USA, 1999
  13. K. Balci and V. Atalay, 'PCA for Gender Estimation: Which Eigenvectors Contribute?' in Proceedings of Sixteenth International Conference on Pattern Recognition, Vol.3. Quebec City, Canada, 2002, pp. 363-366
  14. B. Moghaddam and M. H. Yang, 'Learning Gender with Support Faces,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, pp.707- 711, 2002 https://doi.org/10.1109/34.1000244
  15. R. Brunelli and T. Poggio, 'HyperBF Networks for Gender Classification,' Proceedings of DARPA Image Understanding Workshop, pp.311-314, 1992
  16. A. Colmenarez, B. J. Frey, and T. S. Huang, 'A probabilistic framework for embedded face and facial expression recognition,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol.1. Ft. Collins, CO, USA, 1999, pp. 1592-1597
  17. Y. Shinohara and N. Otsu, 'Facial Expression Recognition Using Fisher Weight Maps,' in Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Vol.100, 2004, pp.499-504
  18. F. Bourel, C. C. Chibelushi, and A. A. Low, 'Robust Facial Feature Tracking,' in British Machine Vision Conference. Bristol, 2000, pp.232-241
  19. K. Morik, P. Brockhausen, and T. Joachims, 'Combining statistical learning with a knowledgebased approach-A case study in intensive care monitoring,' in 16th International Conference on Machine Learning (ICML-99). San Francisco, CA, USA: Morgan Kaufmann, 1999, pp.268-277
  20. S. Singh and N. Papanikolopoulos, 'Vision-based detection of driver fatigue,' Department of Computer Science, University of Minnesota, Technical report 1997
  21. D. N. Metaxas, S. Venkataraman, and C. Vogler, 'Image-Based Stress Recognition Using a Model- Based Dynamic Face Tracking System,' International Conference on Computational Science, pp.813-821, 2004 https://doi.org/10.1007/b97989
  22. M. M. Rahman, R. Hartley, and S. Ishikawa, 'A Passive And Multimodal Biometric System for Personal Identification,' in International Conference on Visualization, Imaging and Image Processing. Spain, 2005, pp.89-92
  23. R. Brunelli and D. Falavigna, 'Person identification using multiple cues,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.17, pp.955-966, 1995 https://doi.org/10.1109/34.464560
  24. M. Viswanathan, H. S. M. Beigi, A. Tritschler, and F. Maali, 'Information access using speech, speaker and face recognition,' in IEEE International Conference on Multimedia and Expo, Vol.1, 2000, pp. 493-496
  25. A. K. Jain, K. Nandakumar, X. Lu, and U. Park, 'Integrating Faces, Fingerprints, and Soft Biometric Traits for User Recognition,' Proceedings of Biometric Authentication Workshop, in conjunction with ECCV2004, LNCS 3087, pp.259-269, 2004
  26. P. Melin and O. Castillo, 'Human Recognition using Face, Fingerprint and Voice,' in Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing, Vol.172, Studies in Fuzziness and Soft Computing: Springer Berlin/Heidelberg, 2005, pp.241-256 https://doi.org/10.1007/b97585
  27. K. Chang, K. W. Bowyer, S. Sarkar, and B. Victor, 'Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, pp.1160-1165, 2003 https://doi.org/10.1109/TPAMI.2003.1227990
  28. R. Chellappa, A. Roy-Chowdhury, and S. Zhou, 'Human Identification Using Gait and Face,' in The Electrical Engineering Handbook, 3rd ed: CRC Press, 2004
  29. S. Ben-Yacoub, J. Luttin, K. Jonsson, J. Matas, and J. Kittler, 'Audio-visual person verification,' in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.1. Fort Collins, CO, USA, 1999, pp.580-585
  30. X. Zhou and B. Bhanu, 'Feature fusion of side face and gait for video-based human identification,' Pattern Recognition, Vol.41, pp.778-795, 2008 https://doi.org/10.1016/j.patcog.2007.06.019
  31. D. Bouchaffra and A. Amira, 'Structural hidden Markov models for biometrics: Fusion of face and fingerprint,' Pattern Recognition, Vol.41, pp.852-867, 2008 https://doi.org/10.1016/j.patcog.2007.06.033
  32. H. Vajaria, T. Islam, P. Mohanty, S. Sarkar, R. Sankar, and R. Kasturi, 'Evaluation and analysis of a face and voice outdoor multi-biometric system,' Pattern Recognition Letters, Vol.28, pp.1572-1580, 2007 https://doi.org/10.1016/j.patrec.2007.03.019
  33. Y.-F. Yao, X.-Y. Jing, and H.-S. Wong, 'Face and palmprint feature level fusion for single sample biometrics recognition,' Neurocomputing, Vol.70, pp. 1582-1586, 2007 https://doi.org/10.1016/j.neucom.2006.08.009
  34. J. Zhou, G. Su, C. Jiang, Y. Deng, and C. Li, 'A face and fingerprint identity authentication system based on multi-route detection,' Neurocomputing, Vol.70, pp.922-931, 2007 https://doi.org/10.1016/j.neucom.2006.10.044
  35. C. Nastar and M. Mitschke, 'Real time face recognition using feature combination,' in Third IEEE International Conference on Automatic Face and Gesture Recognition. Nara, Japan, 1998, pp. 312-317
  36. S. Gong, S. J. McKenna, and A. Psarrou., Dynamic Vision: From Images to Face Recognition: Imperial College Press (World Scientific Publishing Company), 2000
  37. T. Jebara, '3D Pose Estimation and Normalization for Face Recognition,' Center for Intelligent Machines, McGill University, Undergraduate Thesis May, 1996
  38. P. J. Phillips, H. Wechsler, J.Huang, and P. J. Rauss, 'The FERET database and evaluation procedure for face-recognition algorithm,' Image and Vision Computing, Vol.16, pp.295-306, 1998 https://doi.org/10.1016/S0262-8856(97)00070-X
  39. D. Blackburn, J. Bone, and P. J. Phillips, 'Face recognition vendor test 2000,' Defense Advanced Research Projects Agency, Arlington, VA, Technical report A269514, February 16, 2001
  40. P. J. Phillips, P. Grother, R. J. Micheals, D. M. Blackburn, E. Tabassi, and J. M. Bone, 'Face Recognition Vendor Test (FRVT 2002),' National Institute of Standards and Technology, Evaluation report IR 6965, March, 2003
  41. K. Messer, J. Kittler, M. Sadeghi, M. Hamouz, A. Kostin, F. Cardinaux, S. Marcel, S. Bengio, C. Sanderson, J. Czyz, L. Vandendorpe, C. McCool, S. Lowther, S. Sridharan, V. Chandran, R. P. Palacios, E. Vidal, L. Bai, L. Shen, Y. Wang, Y.-H. Chiang, H.-C. Liu, Y.-P. Hung, A. Heinrichs, M. Muller, A. Tewes, C. v. d. Malsburg, R. P. Wurtz, Z. Wang, F. Xue, Y. Ma, Q. Yang, C. Fang, X. Ding, S. Lucey, R. Goss, H. Schneiderman, N. Poh, and Y. Rodriguez, 'Face Authentication Test on the BANCA Database,' in 17th International Conference on Pattern Recognition, Vol.4. Cambridge, UK, 2004, pp.523-532
  42. X. Q. Ding and C. Fang, 'Discussions on some problems in face recognition,' in Advances In Biometric Person Authentication, Proceedings, Vol. 3338, Lecture Notes In Computer Science: Springer Berlin/Heidelberg, 2004, pp.47-56 https://doi.org/10.1007/b104239
  43. J. Yang, X. Chen, and W. Kunz, 'A PDA-based face recognition system,' in Proceedings of sixth IEEE Workshop on Applications of Computer Vision. Orlando, Florida, 2002, pp.19-23
  44. W. Zhao, R. Chellappa, P. Phillips, and A. Rosenfeld, 'Face Recognition: A Literature Survey,' ACM Computing Surveys, Vol.35, pp.399-458, 2003 https://doi.org/10.1145/954339.954342
  45. A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, '2D and 3D face recognition: A survey,' Pattern Recognition Letters, Vol.28, pp.1885-1906, 2007 https://doi.org/10.1016/j.patrec.2006.12.018
  46. R. Brunelli and T. Poggio, 'Face recognition: features versus templates,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, pp.1042- 1052, 1993 https://doi.org/10.1109/34.254061
  47. M. A. Grudin, 'On internal representations in face recognition systems,' Pattern Recognition, Vol.33, pp.1161-1177, 2000 https://doi.org/10.1016/S0031-3203(99)00104-1
  48. B. Heisele, P. Ho, J. Wu, and T. Poggio, 'Face recognition: component-based versus global approaches,' Computer Vision and Image Understanding, Vol.91, pp.6-21, 2003 https://doi.org/10.1016/S1077-3142(03)00073-0
  49. T. Kanade, 'Picture Processing System by Computer Complex and Recognition of Human Faces,' Kyoto University, Japan, PhD. Thesis 1973
  50. A. Yuille, D. Cohen, and P. Hallinan, 'Feature extraction from faces using deformable templates,' in IEEE Computer Society Conference on Computer Vision and Templates. San Diego, CA, USA, 1989, pp.104-109
  51. N. Roeder and X. Li, 'Experiments in analyzing the accuracy of facial feature detection,' Vision Interface '95, pp.8-16, 1995
  52. C. Colombo, A. D. Bimbo, and S. D. Magistris, 'Human-computer interaction based on eye movement tracking,' Computer Architectures for Machine Perception, pp.258-263, 1995
  53. M. Nixon, 'Eye spacing measurement for facial recognition,' in SPIE Proceedings, 1985, pp.279-285
  54. D. Reisfeld, 'Generalized symmetry transforms: attentional mechanisms and face recognition,' Tel-Aviv University, PhD. Thesis, technical report 1994
  55. H. P. Graf, T. Chen, E. Petajan, and E. Cosatto, 'Locating faces and facial parts,' in International Workshop on Automatic Face-and Gesture-Recognition, 1995, pp.41-46
  56. I. Craw, D. Tock, and A. Bennett, 'Finding face features,' in Second European Conference on Computer Vision, 1992, pp.92-96
  57. I. J. Cox, J. Ghosn, and P. N. Yianilos, 'Featurebased face recognition using mixture-distance,' in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1996, pp.209-216
  58. S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, 'Face Recognition: A Convolutional Neural Network Approach,' IEEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition, pp.1-24, 1997
  59. L. Wiskott, J.-M. Fellous, N. Kruger, and C. von der Malsburg, 'Face Recognition by Elastic Bunch Graph Matching,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.19, pp.775-779, 1997 https://doi.org/10.1109/34.598235
  60. M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. v. d. Malsburg, R. P. Wurtz, and W. Konen, 'Distortion invariant object recognition in the dynamic link architecture,' IEEE Trans. Computers, Vol.42, pp.300-311, 1993 https://doi.org/10.1109/12.210173
  61. L. Wiskott, J. M. Fellous, N. Kruger, and C. von der Malsburg, 'Face Recognition by Elastic Bunch Graph Matching,' in Intelligent Biometric Techniques in Fingerprint and Face Recognition, L. C. Jain, U. Halici, I. Hayashi, S. B. Lee, and Jae-Ho, Eds.: CRC Press, 1999, pp.355-396
  62. P. J. Phillips, P. Rauss, and S. Der, 'FERET (FacE REcognition Technology) Recognition Algorithm Development and Test Report,' U.S. Army Research Laboratory ARL-TR-995, 1996
  63. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, 'The FERET Evaluation Methodology for Facerecognition Algorithms,' in Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp.137-143
  64. G. Sukthankar, 'Face recognition: a critical look at biologically-inspired approaches,' Carnegie Mellon University, Pittsburgh, PA, Technical Report: CMURITR-00-04 2000
  65. P. Campadelli and R. Lanzarotti, 'A Face Recognition System Based on Local Feature Characterization,' in Advanced Studies in Biometrics, Vol.3161, Lecture Notes in Computer Science, M. Tistarelli, J. Bigun, and E. Grosso, Eds. Berlin: Springer, 2005, pp.147-152 https://doi.org/10.1007/11493648_10
  66. H. Shin, S. D. Kim, and H. C. Choi, 'Generalized elastic graph matching for face recognition,' Pattern Recognition Letters, Vol.28, pp.1077.1082, 2007 https://doi.org/10.1016/j.patrec.2007.01.003
  67. A. Albiol, D. Monzo, A. Martin, J. Sastre, and A. Albiol, 'Face recognition using HOG. EBGM,' Pattern Recognition Letters, Vol.29, pp.1537-1543, 2008 https://doi.org/10.1016/j.patrec.2008.03.017
  68. L. D. Harmon, M. K. Khan, R. LAsch, and P. F. Raming, 'Machine Identification of human faces,' Pattern Recognition, Vol.13, pp.97-110, 1981 https://doi.org/10.1016/0031-3203(81)90008-X
  69. L. D. Harmon, S. C. Kuo, P. F. Raming, and U. Raudkivi, 'Identification of human face profiles by computers,' Pattern Recognition, Vol.10, pp.301-312, 1978 https://doi.org/10.1016/0031-3203(78)90001-8
  70. G. J. Kaufman and K. J. Breeding, 'Automatic recognition of human faces from profile silhouettes,' IEEE Transactions On Systems Man And Cybernetics, SMC, Vol.6, pp.113-121, 1976
  71. Z. Liposcak and S. Loncaric, 'A scale-space approach to face recognition from profiles,' in Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns, Vol. 1689, Lecture Notes In Computer Science. London, UK: Springer-Verlag, 1999, pp.243-250 https://doi.org/10.1007/3-540-48375-6_30
  72. Z. Liposcak and S. Loncaric, 'Face recognition from profiles using morphological signature transform,' in Proceedings of the 21st Int'l Conference Information Technology Interfaces. Pula, Croatia, 1999, pp.93-98
  73. R. Brunelli and T. Poggio, 'Face Recognition Through Geometrical Features,' in Proceedings of the Second European Conference on Computer Vision, Vol.588, Lecture Notes In Computer Science, G. Sandini, Ed. London, UK: Springer-Verlag, 1992, pp.782-800 https://doi.org/10.1007/3-540-55426-2_90
  74. R. Cendrillon and B. C. Lowell, 'Real-Time Face Recognition using Eigenfaces,' in Proceedings of the SPIE International Conference on Visual Communications and Image Processing, Vol.4067, 2000, pp.269-276
  75. R. J. Baron, 'Mechanisms of Human Facial Recognition,' International Journal of Man-Machine Studies, Vol.15, pp.137-178, 1981 https://doi.org/10.1016/S0020-7373(81)80001-6
  76. R.-J. J. Huang, 'Detection Strategies for face recognition using learning and evolution,' George Mason University, Fairfax, Virginia, Ph. D. Dissertation 1998
  77. L. Sirovich and M. Kirby, 'Low-dimensional Procedure for the Characterization of Human Faces,' Journal of the Optical Society of America A: Optics, Image Science, and Vision, Vol.4, pp.519-524, 1987 https://doi.org/10.1364/JOSAA.4.000519
  78. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. New Jersey: Prentice-Hall, 1988
  79. K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed. Boston, MA: Academic Press, 1990
  80. M. Turk and A. Pentland, 'Face Recognition Using Eigenfaces,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp.586-591
  81. M. Turk and A. Pentland, 'Eigenfaces For Recognition,' Journal Of Cognitive Neuroscience, Vol.3, pp.71-86, 1991 https://doi.org/10.1162/jocn.1991.3.1.71
  82. A. Pentland, B. Moghaddam, and T. Starner, 'Viewbased and modular eigenspaces for face recognition,' in IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp.84-90
  83. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, 'Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.19, pp.711-720, 1997 https://doi.org/10.1109/34.598228
  84. Y. Moses, Y. Adini, and S. Ullman, 'Face recognition: the problem of compensating for changes in illumination direction,' in European Conf. Computer Vision, 1994, pp.286-296 https://doi.org/10.1007/3-540-57956-7_33
  85. R. A. Fisher, 'The use of multiple measures in taxonomic problems,' Annals of Eugenics, Vol.7, pp. 179-188, 1936 https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
  86. D. L. Swets and J. J. Weng, 'Using discriminant eigenfeatures for image retrieval,' IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 18, pp.831-836, 1996 https://doi.org/10.1109/34.531802
  87. A. M. Martinez and A. C. Kak, 'PCA versus LDA,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, pp.228-233, 2001 https://doi.org/10.1109/34.908974
  88. B. Moghaddam, C. Nastar, and A. Pentland, 'A Bayesian Similarity Measure for Direct Image Matching,' in Proceedings 13th International Conference on Pattern Recognition, 1996, pp.350-358 https://doi.org/10.1109/ICPR.1996.546848
  89. B. Moghaddam and A. Pentland, 'Probabilistic visual learning for object representation,' IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol.19, pp.696-710, 1997 https://doi.org/10.1109/34.598227
  90. M. A. O. Vasilescu and D. Terzopoulos, 'Multilinear Subspace Analysis of Image Ensembles,' in Proc. IEEE Int'l Conf. on Computer Vision and Pattern Recognition, 2003, pp.93-99
  91. Q. Yang and X. Q. Ding, 'Symmetrical Principal Component Analysis and Its Application in Face Recognition,' Chinese Journal of Computers, Vol.26, pp.1146.1151, 2003
  92. J. Yang and D. Zhang, 'Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.28, pp.131-137, 2004 https://doi.org/10.1109/TPAMI.2004.1261097
  93. J. Meng and W. Zhang, 'Volume measure in 2DPCAbased face recognition,' Pattern Recognition Letters, Vol.28, pp.1203-1208, 2007 https://doi.org/10.1016/j.patrec.2007.01.015
  94. G. D. C. Cavalcanti and E. C. B. C. Filho, 'Eigenbands Fusion for Frontal Face Recognition,' in Proceedings of IEEE Internationall Conference on Image Processing, Vol.1, 2003, pp.665.668
  95. K. R. Tan and S. C. Chen, 'Adaptively weighted subpattern PCA for face recognition,' Neurocomputing, Vol.64, pp.505-511, 2005 https://doi.org/10.1016/j.neucom.2004.10.113
  96. A. P. Kumar, S. Das, and V. Kamakoti, 'Face recognition using weighted modular principle component analysis,' in Neural Information Processing, Vol.3316, Lecture Notes In Computer Science: Springer Berlin/Heidelberg, 2004, pp.362-367 https://doi.org/10.1007/b103766
  97. V. D. M. Nhat and S. Lee, 'An Improvement on PCA Algorithm for Face Recognition,' in Advances in Neural Networks-ISNN 2005, Vol.3498, Lecture Notes in Computer Science. Chongqing: Springer, 2005, pp.1016-1021
  98. N. Sun, H.-x. Wang, Z.-h. Ji, C.-r. Zou, and L. Zhao, 'An efficient algorithm for Kernel two-dimensional principal component analysis,' Neural Computing & Applications, Vol.17, pp.59-64, 2008 https://doi.org/10.1007/s00521-007-0111-0
  99. D. Zhang, Z.-H. Zhoua, and S. Chen, 'Diagonal principal component analysis for face recognition,' Pattern Recognition, Vol.39, pp.140-142, 2006 https://doi.org/10.1016/j.patcog.2005.08.002
  100. H. Yu and J. Yang, 'A Direct LDA Algorithm for High-dimensional Data with Application to Face Recognition,' Pattern Recognition, Vol.34, pp.2067-2070, 2001 https://doi.org/10.1016/S0031-3203(00)00162-X
  101. F. Song, D. Zhang, J. Wang, H. Liu, and Q. Tao, 'A parameterized direct LDA and its application to face recognition,' Neurocomputing, Vol.71, pp.191-196, 2007 https://doi.org/10.1016/j.neucom.2007.01.003
  102. D. Zhou and X. Yang, 'Face Recognition Using Direct-Weighted LDA,' in 8th Pacific Rim International Conference on Artificial Intelligence. Auckland, New Zealand, 2004, pp.760-768 https://doi.org/10.1007/b99563
  103. L. Chen, H. Liao, M. Ko, L. J., and G. Yu, 'A New LDA-based Face Recognition System Which Can Solve the Small Samples Size Problem,' Journal of Pattern Recognition, Vol.33, pp.1713.1726, 2000 https://doi.org/10.1016/S0031-3203(99)00139-9
  104. W. Liu, Y. Wang, S. Z. Li, and T. Tan, 'Null Space Approach of Fisher Discriminant Analysis for Face Recognition,' in Biometric Authentication, Vol.3087, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2004, pp.32-44 https://doi.org/10.1007/978-3-540-25976-3_4
  105. X. Wang and X. Tang, 'Dual-space Linear Discriminant Analysis for Face Recognition,' in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2004, pp.564.569
  106. M. Loog, R. P. W. Duin, and R. Haeb-Umbach, 'Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, pp.762-766, 2001 https://doi.org/10.1109/34.935849
  107. J. H. Friedman, 'Regularized Discriminant Analysis,' Journal of the American Statistical Association, Vol.84, pp.165-175, 1989 https://doi.org/10.2307/2289860
  108. P. Howland and H. Park, 'Generalized Discriminant Analysis Using the Generalized Singular Value Decomposition,' IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol.26, pp.995.1006, 2004
  109. J. P. Ye, R. Janardan, C. H. Park, and H. Park, 'An Optimization Criterion for Generalized Discriminant Analysis on Undersampled Problems,' IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol.26, pp.982.994, 2004 https://doi.org/10.1109/TPAMI.2004.37
  110. J. W. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, 'Face Recognition Using LDA-based Algorithms,' IEEE Trans. On Neural Networks, Vol.14, pp.195-200, 2003 https://doi.org/10.1109/TNN.2002.806647
  111. J. W. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, 'Boosting Linear Discriminant Analysis for Face Recognition,' in Proceedings of IEEE International Conference on Image Processing, Vol.1, 2003, pp.657-660
  112. Q. Yang and X. Q. Ding, 'Discriminant Local Feature Analysis of Facial Images,' in IEEE International Conference on Image Processing, Vol.2, 2003, pp.863-866
  113. Q. Liu, H. Lu, and S. Ma, 'Improving Kernel Fisher Discriminant Analysis for Face Recognition,' IEEE Transactions on Circuits and Systems for Video Technology, Vol.14, pp.42-49, 2004 https://doi.org/10.1109/TCSVT.2003.818352
  114. B. Scholkopf, 'Nonlinear Component Analysis as a Kernel Eigenvalue Problem,' Neural Computation, Vol.10, pp.1299-1319, 1998 https://doi.org/10.1162/089976698300017467
  115. Q. Liu, X. Tang, H. Lu, and S. Ma, 'Kernel Scatter- Difference Based Discriminant Analysis for Face Recognition,' in Proc. IEEE International Conference on Pattern Recognition, 2004, pp.419-422 https://doi.org/10.1109/ICPR.2004.522
  116. M. Li and B. Yuan, '2D-LDA: A statistical linear discriminant analysis for image matrix,' Pattern Recognition Letters, Vol.26, pp.527-532, 2005 https://doi.org/10.1016/j.patrec.2004.09.007
  117. H. L. Xiong, M. N. S. Swamy, and M. O. Ahmad, 'Two-dimensional FLD for face recognition,' Pattern Recognition, Vol.38, pp.1121-1124, 2005 https://doi.org/10.1016/j.patcog.2004.12.003
  118. X. Y. Jing, Y. Y. Tang, and D. Zhang, 'A Fourier-LDA approach for image recognition,' Pattern Recognition, Vol.38, pp.453-457, 2005 https://doi.org/10.1016/j.patcog.2003.09.020
  119. Y. W. Pang, L. Zhang, M. J. Li, Z. K. Liu, and W. Y. Ma, 'A novel Gabor-LDA based face recognition method,' in Advances In Multimedia Information Processing-Pcm 2004, Pt 1, Proceedings, vol. 3331, Lecture Notes In Computer Science, 2004, pp.352-358 https://doi.org/10.1007/b104114
  120. V. D. M. Nhat and S. Lee, 'Block LDA for Face Recognition,' in Computational Intelligence and Bioinspired Systems, Vol.3512, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2005, pp.899-905 https://doi.org/10.1007/11494669_110
  121. D. Zhou and X. Yang, 'Face Recognition Using Enhanced Fisher Linear Discriminant Model with Facial Combined Feature,' in PRICAI 2004: Trends in Artificial Intelligence, Vol.3157, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2004, pp.769-777 https://doi.org/10.1007/b99563
  122. W. C. Zhang, S. G. Shan, W. Gao, Y. Z. Chang, and B. Cao, 'Component-based cascade linear discriminant analysis for face recognition,' in Advances In Biometric Person Authentication, Proceedings, Vol.3338, Lecture Notes In Computer Science, 2004, pp.288-295 https://doi.org/10.1007/b104239
  123. H. Zhao and P. C. Yuen, 'Incremental Linear Discriminant Analysis for Face Recognition,' IEEE Transactions on Systems, Man & Cybernetics: Part B, Vol.38, pp.210-221, 2008 https://doi.org/10.1109/TSMCB.2007.908870
  124. Y. Chang, C. Hu, and M. Turk, 'Manifold of facial expression,' in IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003, pp.28-35
  125. K.-C. Lee, J. Ho, M.-H. Yang, and D. J. Kriegman, 'Video-Based Face Recognition Using Probabilistic Appearance Manifolds,' Computer Vision and Pattern Recognition, Vol.1, pp.313-320, 2003
  126. S. T. Roweis and L. K. Saul, 'Nonlinear Dimensionality Reduction by Locally Linear Embedding,' Science, Vol.290, pp.2323.2326, 2000 https://doi.org/10.1126/science.290.5500.2323
  127. S. Roweis, L. Saul, and G. E. Hinton, 'Global coordination of local linear models,' Advances in Neural Information Processing Systems, Vol.14, pp.889-896, 2002
  128. H. Seung and D. Lee, 'The Manifold Ways of Perception,' Science, Vol.290, pp.2268-2269, 2000 https://doi.org/10.1126/science.290.5500.2268
  129. A. Shashua, A. Levin, and S. Avidan, 'Manifold Pursuit: A New Approach to Appearance Based Recognition,' in International Conference on Pattern Recognition, Vol.3, 2002, pp.590-594 https://doi.org/10.1109/ICPR.2002.1048008
  130. J. Tenenbaum, V. de Silva, and J. Langford, 'A global geometric framework for nonlinear dimensionality reduction,' Science, Vol.290, pp.2319-2323, 2000 https://doi.org/10.1126/science.290.5500.2319
  131. L. K. Saul and S. T. Roweis, 'Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds,' Journal of Machine Learning Research, Vol.4, pp.119-155, 2003 https://doi.org/10.1162/153244304322972667
  132. M. Belkin and P. Niyogi, 'Laplacian eigenmaps and spectral techniques for embedding and clustering,' Advances in Neural Information Processing Systems, Vol.14, pp.585-591, 2001
  133. X. He, S. C. Yan, Y. X. Hu, and H. J. Zhang, 'Learning a Locality Preserving Subspace for Visual Recognition,' in Proceedings of 9th IEEE International Conference on Computer Vision, Vol. 1, 2003, pp.385-392
  134. S. C. Yan, H. J. Zhang, Y. X. Hu, B. Y. Zhang, and Q. S. Cheng, 'Discriminant Analysis on Embedded Manifold,' in European Conference on Computer Vision, Vol. LNCS 3021: Springer Berlin/Heidelberg, 2004, pp.121-132 https://doi.org/10.1007/b97865
  135. J. Zhang, S. Z. Li, and J. Wang, 'Nearest Manifold Approach for Face Recognition,' in Proc. IEEE International Conference on Automatic Face and Gesture Recognition, 2004, pp.223-228
  136. Y. Wu, K. L. Chan, and L. Wang, 'Face Recognition based on Discriminative Manifold Learning,' in Proc. IEEE Int'l Conf. on Pattern Recognition, Vol.4, 2004, pp.171-174 https://doi.org/10.1109/ICPR.2004.363
  137. X. He, S.-C. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, 'Face Recognition Using Laplacianfaces,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, pp.328-340, 2005 https://doi.org/10.1109/TPAMI.2005.55
  138. P. Comon, 'Independent component analysis.A new concept?' Signal Processing, Vol.36, pp.287-314, 1994 https://doi.org/10.1016/0165-1684(94)90029-9
  139. M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, 'Face recognition by independent component analysis,' IEEE Transactions on Neural Networks, Vol.13, pp.1450-1464, 2002 https://doi.org/10.1109/TNN.2002.804287
  140. B. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, 'Recognizing faces with PCA and ICA,' Computer Vision and Image Understanding: Special Issue on Face Recognition, Vol.91, pp.115-137, 2003 https://doi.org/10.1016/S1077-3142(03)00077-8
  141. C. Liu and H. Wechsler, 'Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition,' in International Conference on Audio and Video Based Biometric Person Authentication. Washington, D.C., 1999, pp.211-216
  142. J. Kim, J. Choi, and J. Yi, 'Face Recognition Based on Locally Salient ICA Information,' in Biometric Authentication Workshop, vol. 3087, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2004, pp.1-9 https://doi.org/10.1007/b99174
  143. J. Yi, J. Kim, J. Choi, J. Han, and E. Lee, 'Face Recognition Based on ICA Combined with FLD,' Biometric Authentication, pp.10-18, 2002 https://doi.org/10.1007/3-540-47917-1_2
  144. T. Martiriggiano, M. Leo, T. D'Orazio, and A. Distante, 'Face Recognition by Kernel Independent Component Analysis,' in The 18th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems. Bari, Italy, 2005, pp.55-58 https://doi.org/10.1007/11504894_7
  145. J. Kim, J. Choi, and J. Yi, 'ICA Based Face Recognition Robust to Partial Occlusions and Local Distortions,' in International Conference on Bioinformatics and its Applications. Fort Lauderdale, Florida, USA, 2004, pp.147-154
  146. K.-C. Kwak and W. Pedrycz, 'Face Recognition Using an Enhanced Independent Component Analysis Approach,' IEEE Transactions on Neural Networks, Vol.18, pp.530-541, 2007 https://doi.org/10.1109/TNN.2006.885436
  147. N. H. Foon, A. T. B. Jin, and D. N. C. Ling, 'Face recognition using wavelet transform and nonnegative matrix factorization,' in Ai 2004: Advances In Artificial Intelligence, Proceedings, Vol.3339, Lecture Notes In Artificial Intelligence, 2004, pp.192-202 https://doi.org/10.1007/b104336
  148. D. D. Lee and H. S. Seung, 'Learning the Parts of Objects by Non-Negative Matrix Factorization,' Nature, Vol.401, pp.788-791, 1999 https://doi.org/10.1038/44565
  149. W. Liu, Y. Wang, S. Z. Li, and T. Tan, 'Nearest Intra-Class Space Classifier for Face Recognition,' in The 17th International Conference on Pattern Recognition (ICPR), Vol.4. Cambridge, UK, 2004, pp.495-498 https://doi.org/10.1109/ICPR.2004.611
  150. J. Li, S. Zhou, and C. Shekhar, 'A Comparison of Subspace Analysis for Face Recognition,' in Proc. IEEE Int'l Conf. on Acoustics, Speech, and Signal Processing, 2003, pp.121.124
  151. Q. Yang and X. Tang, 'Recent Advances in Subspace Analysis for Face Recognition,' SINOBIOMETRICS, pp.275-287, 2004 https://doi.org/10.1007/b104239
  152. D. DeMers and G. W. Cottrell, 'Non-linear dimensionality reduction,' Advances in Neural Information Processing Systems, Vol.5, pp.580-587, 1993
  153. J. Weng, N. Ahuja, and T. S. Huang, 'Learning recognition and segmentation of 3-D objects from 3- D images,' in Proceedings of the International Conference on Computer Vision (ICCV 93). Berlin, Germany, 1993, pp.121-128
  154. T. Kohonen, 'The self-organizing map,' Proceedings of the IEEE, Vol.78, pp.1464-1480, 1990 https://doi.org/10.1109/5.58325
  155. T. Kohonen, Self-organizing maps. Berlin, Germany: Springer-Verlag, 1995
  156. A. Eleyan and H. Demirel, 'Face Recognition System Based on PCA and Feedforward Neural Networks,' in Computational Intelligence and Bioinspired Systems, Vol.3512, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2005, pp.935-942 https://doi.org/10.1007/11494669_115
  157. B. Li and H. Yin, 'Face Recognition Using RBF Neural Networks and Wavelet Transform,' in Advances in Neural Networks. ISNN 2005, vol.3497, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2005, pp.105-111 https://doi.org/10.1007/11427445_18
  158. P. Melin, C. Felix, and O. Castillo, 'Face recognition using modular neural networks and the fuzzy Sugeno integral for response integration,' International Journal Of Intelligent Systems, vol.20, pp.275-291, 2005 https://doi.org/10.1002/int.20066
  159. G. C. Zhang, X. S. Huang, S. Z. Li, Y. S. Wang, and X. H. Wu, 'Boosting local binary pattern (LBP)- based face recognition,' in Advances In Biometric Person Authentication, Proceedings, Vol.3338, Lecture Notes In Computer Science, 2004, pp.179-186 https://doi.org/10.1007/b104239
  160. T. Ojala, M. Pietikainen, and M. Maenpaa, 'Multiresolution gray-scale and rotation invariant texture classification width local binary patterns,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, pp.971.987, 2002 https://doi.org/10.1109/TPAMI.2002.1017623
  161. Y. Freund and R. E. Schapire, 'A decision-theoretic generalization of on-line learning and an application to boosting,' Journal of Computer and System Sciences, Vol.55, pp.119-139, 1997 https://doi.org/10.1006/jcss.1997.1504
  162. T. Ahonen, A. Hadid, and M.Pietikainen, 'Face recognition with local binary patterns,' in Proceedings of the European Conference on Computer Vision, Vol.3021, Lecture Notes in Computer Science. Prague, Czech Republic: Springer, 2004, pp.469-481 https://doi.org/10.1007/b97865
  163. U. Krebel, 'Pairwise classification and support vector machines,' Advance in Kernel Methods . Support Vector Learning, pp.255-268, 1999
  164. T. Hastie and R. Tibshirani, 'Classification by Pairwise Coupling,' The Annals of Statistics, Vol.26, pp.451-471, 1998 https://doi.org/10.1214/aos/1028144844
  165. M. Moreira and E. Mayoraz, 'Improved Pairwise Coupling Classification with Correcting Classifiers,' in Proceedings of the 10th European Conference on Machine Learning, Vol.1398, Lecture Notes In Computer Science. London, UK: Springer-Verlag, 1998, pp.160-171 https://doi.org/10.1007/BFb0026686
  166. H. Li, F. Qi, and S. Wang, 'Face Recognition with Improved Pairwise Coupling Support Vector Machines,' in Computational Intelligence and Bioinspired Systems, Vol.3512, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2005, pp.927-934 https://doi.org/10.1007/11494669_114
  167. Z. Li and S. Tang, 'Face Recognition Using Improved Pairwise Coupling Support Vector Machines,' in Proc. of Intl. Conf. on Neural Information Processing, Vol.2, 2002, pp.876-880 https://doi.org/10.1109/ICONIP.2002.1198185
  168. J. Platt, 'Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods,' in Advances in Large Margin Classifiers, A. J. Smola, P. L. Bartlett, B. Scholkopf, and D. Schuurmans, Eds.: MIT Press, 2000, pp.61-74
  169. H. Q. Li, S. Y. Wang, and F. H. Qi, 'Automatic face recognition by support vector machines,' in Combinatorial Image Analysis, Proceedings, Vol.3322, Lecture Notes In Computer Science, 2004, pp.716-725 https://doi.org/10.1007/b103936
  170. C. J. Burges, 'A Tutorial on Support Vector Machines for Pattern Recognition,' Data Mining and Knowledge Discovery, Vol.2, pp.121-267, 1998 https://doi.org/10.1023/A:1009715923555
  171. G. Dai and C. Zhou, 'Face Recognition Using Support Vector Machines with the Robust Feature,' in Proceedings of IEEE Workshop on Robot and Human Interactive Communication, 2003, pp.49-53
  172. O. Deniz, M. Castrillon, and M. Hernandez, 'Face Recognition Using Independent Component Analysis and Support Vector Machines,' Pattern Recognition Letters, Vol.24, pp.2153-2157, 2003 https://doi.org/10.1016/S0167-8655(03)00081-3
  173. K. Jonsson, J. Matas, J. Kittler, and Y. P. Li, 'Learning Support Vector Machines for Face Verification and Recognition,' in Proceedings of IEEE International Conference on Automatic and Gesture Recognition, 2000, pp.208-213
  174. G. Guo, S. Li, and C. Kapluk, 'Face Recognition by Support Vector Machines,' in Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition. Washington, DC, USA, 2000, pp.196-201
  175. Y. Liang, W. Gong, Y. Pan, W. Li, and Z. Hu, 'Gabor Features-Based Classification Using SVM for Face Recognition,' in Advances in Neural Networks . ISNN 2005, Vol.3497, Lecture Notes in Computer Science. Chongqing: Springer, 2005, pp.118-123 https://doi.org/10.1007/11427445_20
  176. L. R. Rabiner, 'A tutorial on Hidden Markov Models and selected applications in speech recognition,' in Readings in Speech Recognition, vol. 77, A. Waibel and K. Lee, Eds. San Francisco, CA: Morgan Kaufmann, 1989, pp.257-285
  177. F. S. Samaria and A. C. Harter, 'Parameterisation of a stochastic model for human face identification,' in Proceedings of the 2nd IEEE Workshop on Applications of Computer Vision. Sarasota, FL, USA, 1994, pp.138-142
  178. F. S. Samaria, 'Face recognition using Hidden Markov Models,' Trinity College, University of Cambridge, Cambridge, UK, Ph. D. Thesis 1994
  179. A. V. Nefian and M. H. Hayes III, 'Face Recognition using an embedded HMM,' in IEEE International Conference Audio Video Biometric based Person Authentication, 1999, pp.19-24
  180. S. Kuo and O. Agazzi, 'Keyword Spotting in poorly printed documents using pseudo 2-D Hidden Markov Models,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, pp.842-848, 1994 https://doi.org/10.1109/34.308482
  181. C. Liu and H. Wechsler, 'Evolutionary Pursuit and Its Application to Face Recognition,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, pp.570-582, 2000 https://doi.org/10.1109/34.862196
  182. H.-L. Huang, H.-M. Chen, S.-J. Ho, and S.-Y. Ho, 'Advanced Evolutionary Pursuit for Face Recognition,' accepted by Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, 2006
  183. J. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, and S. Z. Li, 'Ensemble-based Discriminant Learning with Boosting for Face Recognition,' IEEE Transactions on Neural Networks, Vol.17, pp.166-178, 2006 https://doi.org/10.1109/TNN.2005.860853
  184. J. Lu and K. N. Plataniotis, 'Boosting face recognition on a large-scale database,' in Proceedings of IEEE International Conference on Image Processing, Vol.2. Rochester, NY, 2002, pp.109-112
  185. R. E. Schapire, 'The boosting approach to machine learning: An overview,' in MSRI Workshop Nonlinear Estimation and Classification, 2002, pp.149-172
  186. F. Roli and J. Kittler, 'Multiple Classifier Systems, Third International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002, Proceedings,' in Lecture Notes in Computer Science, Vol.2364, Lecture Notes in Computer Science: Springer Verlag, 2002
  187. X. Lu, Y. Wang, and A. K. Jain, 'Combining Classifiers for Face Recognition,' in Proc. IEEE International Conference on Multimedia & Expo (ICME 2003). Baltimore, MD, 2003, pp.13-16
  188. C. M. Bishop, Neural Networks for Pattern Recognition: Oxford University Press, UK, 1995
  189. G. L. Marcialis and F. Roli, 'Fusion of LDA and PCA for face recognition,' in Proceedings of the Workshop on Machine Vision and Perception, 8th Workshop of the Italian Association for Artificial Intelligence (ALLA 02), 2002
  190. G. L. Marcialis and F. Roli, 'Fusion of LDA and PCA for face verification,' in Proceedings of the Workshop on Biometric Authentication, Vol.2359, LNCS, M. Tistarelli, J. Bigun, and A. K. Jain, Eds. Copenhagen, Denmark: Springer-Verlag, 2002, pp.30-37 https://doi.org/10.1007/3-540-47917-1_4
  191. G. L. Marcialis and F. Roli, 'Fusion of appearancebased face recognition algorithms,' Pattern Analysis and Applications, Vol.7, pp.151-163, 2004 https://doi.org/10.1007/s10044-004-0212-7
  192. B. Achermann and H. Bunke, 'Combination of Classifiers on the Decision Level for Face Recognition,' Insitut fur Informatik und angewandte Mathematik, Universitat Bern, Bern, Germany, Technical Report IAM-96-002 January 1996
  193. A. S. Tolba and A. N. Abu-Rezq, 'Combined Classifier for Invariant Face Recognition,' Pattern Analysis and Applications, Vol.3, pp.289-302, 2000 https://doi.org/10.1007/s100440070001
  194. Y. H. Wan, S. M. Ji, Y. Xie, X. Zhang, and P. J. Xie, 'Video program clustering indexing based on face recognition hybrid model of hidden Markov model and support vector machine,' in Combinatorial Image Analysis, Proceedings, Vol.3322, Lecture Notes In Computer Science, 2004, pp.739-749 https://doi.org/10.1007/b103936
  195. K. C. Kwak and W. Pedrycz, 'Face recognition: A study in information fusion using fuzzy integral,' Pattern Recognition Letters, Vol.26, pp.719-733, 2005 https://doi.org/10.1016/j.patrec.2004.09.024
  196. T. Murofushi and M. Sugeno, 'An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure,' Fuzzy Sets System, Vol.29, pp.201-227, 1988 https://doi.org/10.1016/0165-0114(89)90194-2
  197. J. Haddadnia, K. Faez, and M. Ahmadi, 'N-Feature Neural Network Human Face Recognition,' Image and Vision Computing, Vol.22, pp.1071-1082, 2002 https://doi.org/10.1016/j.imavis.2004.03.011
  198. J. Haddadnia, K. Faez, and P. Moallem, 'Neural network based face recognition with moment invariants,' in IEEE International Conference on Image Processing, Vol.1. Thessaloniki, Greece, 2001, pp.1018-1021
  199. J. Haddadnia, M. Ahmadi, and K. Faez, 'An Efficient Method for Recognition of Human Face Recognition Using Higher Order Pseudo Zernike Moment Invariant,' in The 5th IEEE Int. Conf. on Automatic Face and Gesture Recognition. Washington, DC, USA, 2002
  200. C. Beumier and M. Acheroy, 'Automatic Face Recognition,' in Proceedings symposium IMAGING. Eindhoven, The Netherlands, 2000, pp.77-89
  201. L. Torres, L. Lorente, and J. Vila, 'Face recognition using self-eigenfaces,' in International Symposium on Image/Video Communications Over Fixed and Mobile Networks. Rabat, Morocco, 2000, pp.44-47
  202. R. Chellappa, C. L. Wilson, and S. Sirohey, 'Human and machine recognition of faces: A survey,' Proceedings of the IEEE, Vol.83, pp.705-740, 1995 https://doi.org/10.1109/5.381842
  203. A. Howell and H. Buxton, 'Towards unconstrained face recognition from image sequences,' in Proceedings of the Second IEEE International Conference on Automatic Face and Gesture Recognition, 1996, pp.224-229
  204. J. Moody and C. Darken, 'Learning with localized receptive fields,' in Proceedings of the 1988 Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, Eds.: Morgan Kaufmann, 1988, pp.133-143
  205. J. Moody and C. Darken, 'Fast learning in networks of locally-tuned processing units,' Neural Computation, Vol.1, pp.281-294, 1989 https://doi.org/10.1162/neco.1989.1.2.281
  206. S. McKenna and S. Gong, 'Combined motion and model-based face tracking,' in Proceedings of British Machine Vision Conference. Edinburgh, UK, 1996, pp.755-765
  207. T. E. de Campos, R. S. Feris, and R. M. Cesar Jr., 'A Framework for Face Recognition from Video Sequences Using GWN and Eigenfeature Selection,' in Workshop on Artificial Intelligence and Computer Vision. Atibaia, Brazil, 2000
  208. R. S. Feris, T. E. Campos, and R. M. Cesar Jr., 'Detection and Tracking of Facial Features in Video Sequences,' in Mexican International Conference on Artificial Intelligence (MICAI 2000), Vol.1793, Lecture Notes in Artificial Intelligence. Acapulco, Mexico: Springer-Verlag, 2000, pp.129-137
  209. V. Kruger and G. Sommer, 'Affine real-time face tracking using a wavelet network,' in ICCV'99 Workshop: Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems. Corfu, Greece, 1999, pp.141-148
  210. T. E. de Campos, I. Bloch, and R. M. Cesar Jr., 'Feature Selection Based on Fuzzy Distances Between Clusters: First Results on Simulated Data,' in Proceedings of International Conference on Advances on Pattern Recognition-ICAPR'2000, Vol.2013, Lecture Notes In Computer Science. Rio de Janeiro, Brasil: Springer-Verlag, 2001, pp.186-195 https://doi.org/10.1007/3-540-44732-6_19
  211. R. Duda and P. Hart, Pattern Classification and Scene Analysis. New York, USA: Wiley, 1973
  212. A. K. Jain, R. P. W. Duin, and J. Mao, 'Statistical Pattern Recognition: A Review,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, pp.4-37, 2000 https://doi.org/10.1109/34.824819
  213. Z. Biuk and S. Loncaric, 'Face recognition from multi-pose image sequence,' in Proceedings of 2nd IEEE R8-EURASIP Int'l Symposium on Image and Signal Processing and Analysis. Pula, Croatia, 2001, pp.319-324
  214. V. Krueger and S. Zhou, 'Exemplar-based face recognition from video,' in Computer Vision-ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002. Proceedings, Part IV, Vol.2353, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2002, pp.732
  215. S. Zhou, V. Krueger, and R. Chellappa, 'Face Recognition from Video: A CONDENSATION Approach,' in Proc. of Fifth 1EEE International Conference on Automatic Face and Gesture Recognition. Washington D.C., USA, 2002, pp.221-228
  216. G. L. Marcialis and F. Roli, 'Fusion of face recognition algorithms for video-based surveillance systems,' in Multisensor Surveillance Systems: The Fusion Perspective, G. L. Foresti, C. S. Regazzoni, and P. K. Varshney, Eds.: Kluwer, 2003, pp.235-250
  217. J. Steffens, E. Elagin, and H. Neven, 'Person Spotter . fast and robust system for human detection, tracking and recognition,' in Proceedings, International Conference on Audio-and Video-Based Person Authentication, 1999, pp.96-101
  218. presented at Proceedings of the International Conferences on Audio-and Video-Based Person Authentication, 1997-2005
  219. S. Zhou and R. Chellappa, 'Beyond a single still image: Face recognition from multiple still images and videos,' in Face Processing: Advanced Modeling and Methods: Academic Press, 2005
  220. M. C. Chiang and T. E. Boult, 'Local Blur Estimation and Super-resolution,' in Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp.821-826
  221. K. Aizawa, T. Komatsu, and T. Saito, 'A scheme for acquiring very high resolution images using multiple cameras,' in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol.3, 1992, pp.289-292
  222. M. Elad and A. Feuer, 'Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images,' IEEE Trans. Image Processing, Vol.6, pp.1646-1658, 1997 https://doi.org/10.1109/83.650118
  223. M. Berthod, H. Shekarforoush, M. Werman, and J. Zerubia, 'Reconstruction of high resolution 3D visual information using sub-pixel camera displacements,' in IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp.654-657
  224. M. H. Yang, D. Kriegman, and N. Ahuja, 'Detecting faces in images: a survey,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, pp.34-58, 2002 https://doi.org/10.1109/34.982883
  225. L. Torres, 'Is there any hope for face recognition?' in Proc. of the 5th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2004). Lisboa, Portugal, 2004
  226. A. Tibbalds, 'Three Dimensional Human Face Acquisition for Recognition,' Trinity College, University of Cambridge, Cambridge, Ph. D. Thesis March 1998
  227. C. Hesher, A. Srivastava, and G. Erlebacher, 'A novel technique for face recognition using range imaging,' in Proceedings of the 7th IEEE International Symposium on Signal Processing and Its Applications, Vol.2, 2003, pp.201-204 https://doi.org/10.1109/ISSPA.2003.1224850
  228. G. Gordon, 'Face Recognition Based on Depth Maps and Surface Curvature,' in SPIE Proceedings: Geometric Methods in Computer Vision, Vol.1570, 1991, pp.234-247 https://doi.org/10.1117/12.48428
  229. Cyberware, 'Cyberware Inc.: Electronic Documentation.'
  230. Scanners, '3D Scanners Ltd. Electronic Documentation, from http://www.3dscanners.com/.HTML.'
  231. U. Castellani, M. Bicego, G. Iacono, and V. Murino, '3D Face Recognition Using Stereoscopic Vision,' in Advanced Studies in Biometrics, Vol.3161, Lecture Notes in Computer Science, M. Tistarelli, J. Bigun, and E. Grosso, Eds.: Springer Berlin/Heidelberg, 2005, pp.126-137 https://doi.org/10.1007/11493648_8
  232. S. Lee, G. Wolberg, and S. Y. Shin, 'Scattered data interpolation with multilevel B-Splines,' IEEE Transactions on Visualization and Computer Graphics, Vol.3, pp.228-244, 1997 https://doi.org/10.1109/2945.620490
  233. G. Pan, Z. Wu, and Y. Pan., 'Automatic 3D face verification from range data,' in International Conference on Acoustics, Speech, and Signal Processing, Vol.3, 2003, pp.193-196
  234. C. Xu, Y. Wang, T. Tan, and L. Quan, 'Automatic 3D face recognition combining global geometric features with local shape variation information,' in International Conference on Automated Face and Gesture Recognition, 2004, pp.308.313
  235. Y. Lee, H. Song, U. Yang, H. Shin, and K. Sohn, 'Local feature based 3D face recognition,' in Audioand Video-Based Biometric Person Authentication, Vol.3546, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2005, pp.909-918 https://doi.org/10.1007/11527923_95
  236. F. R. Al-Osaimi, M. Bennamoun, and A. Mian, 'Integration of local and global geometrical cues for 3D face recognition,' Pattern Recognition, Vol.41, pp.1030-1040, 2008 https://doi.org/10.1016/j.patcog.2007.07.009
  237. J. Y. Cartoux, J. T. LaPreste, and M. Richetin, 'Face authentication or recognition by profile extraction from range images,' in Proceedings of the Workshop on Interpretation of 3D Scenes, 1989, pp.194-199
  238. T. Nagamine, T. Uemura, and I. Masuda, '3D facial image analysis for human identification,' in Proceedings of International Conference on Pattern Recognition (ICPR' 1992). The Hague, Netherlands, 1992, pp.324-327
  239. C. Li and A. Barreto, 'Profile-Based 3D Face Registration and Recognition,' in Advances in Neural Networks. ISNN 2005, Vol.3497, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2005, pp.478-488 https://doi.org/10.1007/11427445_78
  240. Y. Wu, G. Pan, and Z. Wu, 'Face Authentication based on Multiple Profiles Extracted from Range Data,' in 4th International Conference on Audioand Video-based Biometric Person Authentication, Vol. LNCS-2688, 2003, pp.515-522 https://doi.org/10.1007/3-540-44887-X_61
  241. B. Gokberk, A. A. Salah, and L. Akarun, 'Rank- Based Decision Fusion for 3D Shape-Based Face Recognition,' in Proceedings of the 5th International Conference on Audio and Video-based Biometric Person Authentication, Vol.3546, Lecture Notes in Computer Science, 2005, pp.1019-1028 https://doi.org/10.1007/11527923_106
  242. J.-G. Wang, K.-A. Toh, and R. Venkateswarlu, 'Fusion of Appearance and Depth Information for Face Recognition,' in Fifth International Conference on Audio-and Video-Based Biometric Person Authentication, Vol.3546, Lecture Notes in Computer Science: Springer, 2005, pp.919-928 https://doi.org/10.1007/11527923_96
  243. F. Tsalakanidou, D. Tzocaras, and M. Strintzis, 'Use of depth and colour eigenfaces for face recognition,' Pattern Recognition Letters, Vol.24, pp.1427.1435, 2003 https://doi.org/10.1016/S0167-8655(02)00383-5
  244. K. Chang, K. Bowyer, and P. Flynn, 'Face recognition using 2D and 3D facial data,' in Multimodal User Authentication Workshop. Nice, France, 2003, pp.25-32
  245. C. Beumier and M. Acheroy, 'Face verification from 3D and grey level clues,' Pattern Recognition Letters, Vol.22, pp.1321-1329, 2001 https://doi.org/10.1016/S0167-8655(01)00077-0
  246. P. Besl and N. McKay, 'A method for registration of 3D shapes,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.14, pp.239.256, 1992 https://doi.org/10.1109/34.121791
  247. T. Papatheodorou and D. Reuckert, 'Evaluation of automatic 4D face recognition using surface and texture registration,' in International Conference on Automated Face and Gesture Recognition, 2004, pp.321.326
  248. X. Lu and A. K. Jain, 'Integrating range and texture information for 3D face recognition,' in Proceedings of 7th IEEE Workshop on Applications of Computer Vision. Breckenridge, CO, 2005, pp.156-163 https://doi.org/10.1109/ACVMOT.2005.64
  249. Y. Wang, C. Chua, and Y. Ho, 'Facial feature detection and face recognition from 2D and 3D images,' Pattern Recognition Letters, Vol.23, pp.1191-1202, 2002 https://doi.org/10.1016/S0167-8655(02)00066-1
  250. C. Benabdelkader and P. A. Griffin, 'Comparing and combining depth and texture cues for face recognition,' Image And Vision Computing, Vol.23, pp.339-352, 2005 https://doi.org/10.1016/j.imavis.2004.09.004
  251. A. Ruifrok, A. Scheenstra, and R. C. Veltkamp, 'A Survey of 3D Face Recognition Methods,' in Audioand Video-based Biometric Person Authentication, Vol.3546, Lecture Notes in Computer Science: Springer Berlin/Heidelberg, 2005, pp.891-899 https://doi.org/10.1007/11527923_93
  252. K. W. Bowyer, K. Chang, and P. J. Flynn, 'A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,' Computer Vision and Image Understanding, Vol.101, pp.1-15, 2006 https://doi.org/10.1016/j.cviu.2005.05.005
  253. L. B. Wolff, D. A. Socolinsky, and C. K. Eveland, 'Quantitative measurement of illumination invariance for face recognition using thermal infrared imagery,' in CVPR workshop on Computer Vision Beyond Visual Spectrum. Kauai, HI, USA, 2001
  254. R. Cutler, 'Face recognition using infrared images and eigenfaces,' University of Maryland at College Park, College Park, MD, USA, Technical report CSC 989, 1996
  255. A. Gyaourova, G. Bebis, and I. Pavlidis, 'Fusion of Infrared and Visible Images for Face Recognition,' in Computer Vision-ECCV 2004, Vol.3024, Lecture Notes in Computer Science: Springer Berlin / Heidelberg, 2004, pp.456-468 https://doi.org/10.1007/978-3-540-24673-2_37
  256. A. Selinger and D. A. Socolinsky, 'Appearance- Based Facial Recognition Using Visible and Thermal Imagery: A Comparative Study,' Equinox Corporation, Technical Report 02-01 February 2002
  257. S.-Q. Wu, W. Song, L.-J. Jiang, S. L. Xie, F. Pan, W.- Y. Yau, and S. Ranganath, 'Infrared Face Recognition by Using Blood Perfusion Data,' in Audio- and Video-Based Biometric Person Authentication,5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings, Vol.3546, Lecture Notes in Computer Science: Springer, 2005, pp.320-328 https://doi.org/10.1007/11527923_33
  258. J. Wilder, P. J. Phillips, C. H. Jiang, and S. Wiener, 'Comparison of Visible and Infra-red Imagery for Face Recognition,' in Proceedings, International Conference on Automatic Face and Gesture Recognition. Killington, VT, USA, 1996, pp.182-187
  259. D. Socolinsky, L. Wolff, J. Neuheisel, and C. Eveland, 'Illumination invariant face recognition using thermal infrared imagery,' in IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, Vol.1. Kauai, HI, USA, 2001, pp.527-534
  260. T. Sim, R. Sukthankar, M. Mullin, and S. Baluja, 'Memory-based Face Recognition for Visitor Identification,' in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp.214-220
  261. X. Chen, P. Flynn, and K. Bowyer, 'IR and Visible Light Face Recognition,' Computer Vision and Image Understanding, Vol.99, pp.332-358, 2005 https://doi.org/10.1016/j.cviu.2005.03.001
  262. X. Chen, P. Flynn, and K. Bowyer, 'Visible-light and infrared face recognition,' in Proceedings of the Workshop on Multimodal User Authentication. Santa Barbara, California, USA, 2003, pp.48.55
  263. Identix, 'Identix Inc.: Electronic Documentation.'
  264. J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas, 'On Combining Classifiers,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.20, pp.226-239, 1998 https://doi.org/10.1109/34.667881
  265. R. Singh, M. Vatsa, and A. Noore, 'Hierarchical fusion of multi-spectral face images for improved recognition performance,' Information Fusion, Vol.9, pp.200-210, 2008 https://doi.org/10.1016/j.inffus.2006.06.002
  266. J. Heo, S. Kong, B. Abidi, and M. Abidi, 'Fusion of visual and thermal signatures with eyeglass removal for robust face recognition,' in Proceedings of the IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum in Conjunction with CVPR, 2004, 2004, pp.94-99
  267. R. Singh, M. Vatsa, and A. Noore, 'Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition,' Pattern Recognition, Vol.41, pp.880-893, 2008 https://doi.org/10.1016/j.patcog.2007.06.022
  268. S. G. Kong, J. Heo, B. R. Abidi, J. Paik, and M. A. Abidi, 'Recent advances in visual and infrared face recognition-a review,' Computer Vision And Image Understanding, Vol.97, pp.103-135, 2005 https://doi.org/10.1016/j.cviu.2004.04.001

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