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Stacked Progressive Auto-Encoders for Clothing-Invariant Gait Recognition

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 10425)

Abstract

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.

Keywords

Gait recognition Gait Energy Image (GEI) Clothing-invariant Stacked progressive auto-encoders (SPAE) 

Notes

Acknowledgment

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.

References

  1. 1.
    Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: ICML Unsupervised and Transfer Learning, vol. 27(37–50), p. 1 (2012)Google Scholar
  2. 2.
    Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 437–478. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-35289-8_26 CrossRefGoogle Scholar
  3. 3.
    Bengio, Y., et al.: Learning deep architectures for ai. Foundations and trends®. Mach. Learn. 2(1), 1–127 (2009)Google Scholar
  4. 4.
    Bouchrika, I., Carter, J.N., Nixon, M.S.: Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras. Multimedia Tools Appl. 75(2), 1201–1221 (2016)CrossRefGoogle Scholar
  5. 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
  6. 6.
    Hossain, M.A., Makihara, Y., Wang, J., Yagi, Y.: Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recogn. 43(6), 2281–2291 (2010)CrossRefGoogle Scholar
  7. 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. 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. 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
  10. 10.
    Lee, T.K., Belkhatir, M., Sanei, S.: A comprehensive review of past and present vision-based techniques for gait recognition. Multimed. Tools Appl. 72(3), 2833–2869 (2014)CrossRefGoogle Scholar
  11. 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
  12. 12.
    Man, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)CrossRefGoogle Scholar
  13. 13.
    Nandy, A., Chakraborty, R., Chakraborty, P.: Cloth invariant gait recognition using pooled segmented statistical features. Neurocomputing 191, 117–140 (2016)CrossRefGoogle Scholar
  14. 14.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  15. 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. 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

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of EngineeringShinshu UniversityNaganoJapan

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