Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Facial Local Region Based Deep Convolutional Neural Networks for Automated Face Recognition

자동 얼굴인식을 위한 얼굴 지역 영역 기반 다중 심층 합성곱 신경망 시스템

  • Kim, Kyeong-Tae (Division of Computer and Electronic Systems Eng., Hankuk University of Foreign Studies) ;
  • Choi, Jae-Young (Division of Computer and Electronic Systems Eng., Hankuk University of Foreign Studies)
  • 김경태 (한국외국어대학교 컴퓨터 전자시스템 공학부) ;
  • 최재영 (한국외국어대학교 컴퓨터 전자시스템 공학부)
  • Received : 2018.02.23
  • Accepted : 2018.04.20
  • Published : 2018.04.28
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Abstract

In this paper, we propose a novel face recognition(FR) method that takes advantage of combining weighted deep local features extracted from multiple Deep Convolutional Neural Networks(DCNNs) learned with a set of facial local regions. In the proposed method, the so-called weighed deep local features are generated from multiple DCNNs each trained with a particular face local region and the corresponding weight represents the importance of local region in terms of improving FR performance. Our weighted deep local features are applied to Joint Bayesian metric learning in conjunction with Nearest Neighbor(NN) Classifier for the purpose of FR. Systematic and comparative experiments show that our proposed method is robust to variations in pose, illumination, and expression. Also, experimental results demonstrate that our method is feasible for improving face recognition performance.

본 논문에서는 얼굴인식 성능 향상을 위해 얼굴 지역 영역 영상들로 학습된 다중개의 심층 합성곱 신경망(Deep Convolutional Neural Network)으로부터 추출된 심층 지역 특징들(Deep local features)을 가중치를 부여하여 결합하는 방법을 제안한다. 제안 방법에서는 지역 영역 집합으로 학습된 다중개의 심층 합성곱 신경망으로부터 추출된 심층 지역 특징들과 해당 지역 영역의 중요도를 나타내는 가중치들을 결합한 특징표현인 '가중치 결합 심층 지역 특징'을 형성한다. 일반화 얼굴인식 성능을 극대화하기 위해, 검증 데이터 집합(validation set)을 사용하여 지역 영역에 해당하는 가중치들을 계산하고 가중치 집합(weight set)을 형성한다. 가중치 결합 심층 지역 특징은 조인트 베이시안(Joint Bayesian) 유사도 학습방법과 최근접 이웃 분류기(Nearest Neighbor classifier)에 적용되어 테스트 얼굴영상의 신원(identity)을 분류하는데 활용된다. 제안 방법은 얼굴영상의 자세, 표정, 조명 변화에 강인하고 기존 최신 방법들과 비교하여 얼굴인식 성능을 향상시킬 수 있음이 체계적인 실험을 통해 검증되었다.

Keywords

References

  1. Y. C. Hwang, H. J. Mun & J. W. Lee. (2015). Face Recognition System Technologies for Authentication System - A Survey, Journal of Digital Convergence, 5(3), 9-13.
  2. G. J. Lee, J. Y. Jeong, B. H. Hwang & M. R. Choi. (2014). A facial expressions recognition algorithm using image area segmentation and face element. Journal of Digital Convergence, 12(12), 243-248. DOI : 10.14400/JDC.2014.12.12.243
  3. Y. Adini, Y. Moses & S. Ullman. (1997). Face Recognition: The problem of compensating for changes in illumination direction, IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(7), 721-732. DOI : 10.1109/34.598229
  4. S. H. Kim, H. H. Kim & H. S. Lee. (2013). An Improved Face Recognition Method Using SIFT-Grid. Journal of Digital Convergence, 11(2), 299-307. https://doi.org/10.14400/JDPM.2013.11.2.299
  5. R. Collobert et al. (2011). Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12(Aug), 2493- 2537.
  6. G. E. Hinton, et al. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554. DOI : 10.1162/neco.2006.18.7.1527
  7. Y. Lecun, L. Bottou, Y. Bengio & P. Haffner. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. DOI : 10.1109/5.726791
  8. Y. Wen, K. Zhang, Z. Li, & Y. Qiao. (2016). A discriminative feature learning approach for deep face recognition. In European Conference on Computer Vision, 499-515. DOI : 10.1007/978-3-319-46478-7_31
  9. C. Ding & T. Dacheng. (2015). Robust face recognition via multimodal deep face representation. IEEE Transactions on Multimedia, 17(11), 2049-2058. DOI : 10.1109/TMM.2015.2477042
  10. Y. Taigman, M. Yang, M. Ranzato & L. Wolf. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 1701-1708. DOI : 10.1109/CVPR.2014.220
  11. C. Lu & X. Tang. (2015). Surpassing human-level face verification performance on LFW with GaussianFace. In: AAAI, 3811-3819.
  12. L. Wolf, T. Hassner & I. Maoz. (2011). Face recognition in unconstrained videos with matched background similarity. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 529-534. DOI : 10.1109/CVPR.2011.5995566
  13. Y. Sun, Y. Chen, X. Wang & X. Tang. (2014). Deep learning face representation by joint identificationverification. in Advances in Neural Information Processing Systems, 1988-1996.
  14. Y. Sun, X. Wang & X. Tang. (2008). Deeply learned face representations are sparse, selective, and robust. perception, 31, 411-438.
  15. F. Schroff, D. Kalenichenko & J. Philbin. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, 815-823. arXiv preprint arXiv:1503.03832
  16. O. M. Parkhi, A. Vedaldi & A. Zisserman. (2015). Deep face recognition. In Proc. British Machine Vision Conference (BMVC), 1(3), 6.
  17. J. Gu et al. (2017). Recent advances in convolutional neural networks. Pattern Recognition.
  18. L. Best-Rowden, H. Han, C. Otto, B. Klare & A.K. Jain. (2014). Unconstrained face recognition: Identifying a person of interest from a media collection. IEEE Trans. Information Forensics and Security, 9(12), 2144-2157. DOI : 10.1109/TIFS.2014.2359577
  19. D. Chen, X. Cao, L. Wang, F. Wen & J. Sun. (2012). Bayesian face revisited: A joint formulation. European Conf. Computer Vision (ECCV), 566-579. DOI : 10.1007/978-3-642-33712-3_41
  20. X. Wang & X. Tang. (2014). A unified framework for subspace face recognition. Trans. Pattern Anal. Mach. Intell., 26(9), 1222-1228. DOI : 10.1109/TPAMI.2004.57
  21. G. B. Huang, M. Ramesh, T. Berg & E. Learned-miller. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, 1(2), 3.
  22. P. J. Phillips, H. Moon, S. A. Rizvi & P. J. Rauss. (2000). The FERET evaluation methodology for facerecognition algorithms. IEEE Transactions on pattern analysis and machine intelligence, 22(10), 1090-1104. DOI : 10.1109/34.879790
  23. S. Liao & A.K. Jain. (2011). Partial Face Recognition: An Alignment Free Approach. Proceeding of International Joint Conference on Biometrics, 35(5), 1-8. DOI : 10.1109/IJCB.2011.6117573
  24. P. Viola & M. Jones. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, I-511-I518. DOI : 10.1109/CVPR.2001.990517
  25. X. Tan & B. Triggs. (2007). Fusing GABOR and LBP feature sets for Kernel-based face recognition. in Proc. Int. Conf. Anal. Model. Faces Gestures, 235-249. DOI : 10.1007/978-3-540-75690-3_18
  26. S. Xie, S. Shan, X. Chen & J. Chen. (2010). Fusing local patterns of Gabor magnitude and phase for face recognition. IEEE Trans. Image Process., 19(5), 1349-1361. DOI : 10.1109/TIP.2010.2041397
  27. S. U. Hussain, T. Napoleon & F. Jurie. (2012). Face recognition using local quantized patterns. in Proc. Int. Conf. Biometrics, 1-11.
  28. Z. Lei, M. Pietikainen & S. Z. Li. (2014). Learning discriminant face descriptor. IEEE Trans. Pattern Anal. Mach. Intell., 36(2), 289-302. DOI : 10.1109/TPAMI.2013.112
  29. D. Yi, Z. Lei, S. Liao & S. Z. Li. (2014). Learning face representation from scratch.
  30. C. Ding, J. Choi, D. Tao & L. S. Davis. (2016). Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE transactions on pattern analysis and machine intelligence, 38(3), 518-531. DOI : 10.1109/TPAMI.2015.2462338
  31. S. Xie, S. Shan, X. Chen & J. Chen. (2010). Fusing local pattern of Gabor magnitude and phase for face recognition, IEEE trans. image process, 19(5), 1349-1361. DOI : 10.1109/TIP.2010.2041397
  32. T. Ahonen, A. Hadid & M. Pietikainen. (2006). Face description with local binary pattern: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12), 2037-2041. DOI : 10.1109/TPAMI.2006.244
  33. H. Cevikalp, M. Neamtu & M. Wilkes. (2006). Discriminative common vector method with kernels. IEEE Transactions on Neural Networks, 17(6), 1550-1565. DOI : 10.1109/TNN.2006.881485
  34. S. Hussain & B. Triggs. (2012). Visual recognition using local quantized patterns. In Proceedings of the 10th European Conference on Computer Vision, 716-729. DOI : 10.1007/978-3-642-33709-3_51