Stacked Progressive Auto-Encoders for Clothing-Invariant Gait Recognition
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Gait recognition has been considered as an unique and useful biometric for person identification at distance. However, variations in covariate factors such as view angles, clothing, and carrying condition can alter an individual’s gait pattern. These variations make the task of gait analysis much more complicated. Recognizing different subjects under clothing variations remains one of the most challenging tasks in gait recognition. In this paper, we propose a Stacked Progressive Auto-encoders (SPAE) model for clothing-invariant gait recognition. A key contribution of this work is to directly learn clothing-invariant gait features for gait recognition in a progressive way by stacked multi-layer auto-encoders. In each progressive auto-encoder, our SPAE is designed to transform the Gait Energy Images (GEI) with complicated clothing types to ones of normal clothing, while keeping the GEI with normal clothing type unchanged. As a result, it gradually reduces the effect of appearance changes due to variations of clothes. The proposed method is evaluated on the challenging clothing-invariant gait recognition OU-ISIR Treadmill dataset B. The experimental results demonstrate that the proposed method can achieve a far better performance compared to existing works.
KeywordsGait recognition Gait Energy Image (GEI) Clothing-invariant Stacked progressive auto-encoders (SPAE)
The authors would like to express our sincere thanks to Institute of Scientific and Industrial Research, Osaka University for providing access to the OU-ISIR Gait Treadmill-B dataset for the use in this work.
- 1.Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: ICML Unsupervised and Transfer Learning, vol. 27(37–50), p. 1 (2012)Google Scholar
- 3.Bengio, Y., et al.: Learning deep architectures for ai. Foundations and trends®. Mach. Learn. 2(1), 1–127 (2009)Google Scholar
- 5.Guan, Y., Li, C.T., Hu, Y.: Robust clothing-invariant gait recognition. In: 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 321–324. IEEE (2012)Google Scholar
- 7.Islam, M.S., Islam, M.R., Akter, M.S., Hossain, M., Molla, M.: Window based clothing invariant gait recognition. In: 2013 International Conference on Advances in Electrical Engineering (ICAEE), pp. 411–414. IEEE (2013)Google Scholar
- 8.Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
- 9.Kan, M., Shan, S., Chang, H., Chen, X.: Stacked progressive auto-encoders (spae) for face recognition across poses. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1883–1890 (2014)Google Scholar
- 11.Makihara, Y., Matovski, D.S., Nixon, M.S., Carter, J.N., Yagi, Y.: Gait recognition: Databases, representations, and applications. Wiley Encyclopedia of Electrical and Electronics Engineering (2015)Google Scholar
- 15.Yeoh, T.W., Zapotecas-Martínez, S., Akimoto, Y., Aguirre, H.E., Tanaka, K.: Feature selection in gait classification using geometric PSO assisted by SVM. In: Azzopardi, G., Petkov, N. (eds.) CAIP 2015. LNCS, vol. 9257, pp. 566–578. Springer, Cham (2015). doi: 10.1007/978-3-319-23117-4_49 CrossRefGoogle Scholar
- 16.Yeoh, T., Aguirre, H.E., Tanaka, K.: Clothing-invariant gait recognition using convolutional neural network. In: 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 1–5. IEEE (2016)Google Scholar