Abstract
In order to extract the gait spatial-temporal feature, we propose a novel Long-Short Term Memory (LSTM) network for gait recognition in this paper. Given a gait sequence, a CNNs unit with three layers convolution neural networks is used to extract the spatial feature. Then the spatial feature vector is sent to the LSTM unit, which is used to extract the temporal feature. Based on the spatial-temporal feature vector, the triplet loss function is adopted to optimize the network parameters. The CNNs and LSTM unit are jointly trained to act as a gait spatial-temporal feature extractor for the gait recognition system. Finally extensive evaluations are carried out on the CASIA-B dataset. The results turn out that our network performs better than previous state-of-the art method. It shows great potential for the practical application.
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Acknowledgements
This work was supported by the National Science Fund of China under Grants (No. 61472244, No. U1536203, No. 61272409), in part by the Major Scientific and Technological Innovation Project of Hubei Province under Grant No. 2015AAA013.
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Tong, S., Fu, Y., Ling, H., Zhang, E. (2017). Gait Identification by Joint Spatial-Temporal Feature. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_49
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DOI: https://doi.org/10.1007/978-3-319-69923-3_49
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