Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Gait Recognition Based on GF-CNN and Metric Learning

  • Wen, Junqin (Digital Information Technology Institute, Zhejiang Technical Institute of Economics)
  • Received : 2019.05.09
  • Accepted : 2019.10.06
  • Published : 2020.10.31
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Abstract

Gait recognition, as a promising biometric, can be used in video-based surveillance and other security systems. However, due to the complexity of leg movement and the difference of external sampling conditions, gait recognition still faces many problems to be addressed. In this paper, an improved convolutional neural network (CNN) based on Gabor filter is therefore proposed to achieve gait recognition. Firstly, a gait feature extraction layer based on Gabor filter is inserted into the traditional CNNs, which is used to extract gait features from gait silhouette images. Then, in the process of gait classification, using the output of CNN as input, we utilize metric learning techniques to calculate distance between two gaits and achieve gait classification by k-nearest neighbors classifiers. Finally, several experiments are conducted on two open-accessed gait datasets and demonstrate that our method reaches state-of-the-art performances in terms of correct recognition rate on the OULP and CASIA-B datasets.

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References

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