Person Re-id by Incorporating PCA Loss in CNN

  • Kaixuan Zhang
  • Yang Xu
  • Li SunEmail author
  • Song Qiu
  • Qingli Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10705)


This paper proposes an algorithm, particularly a loss function and its end to end learning manner, for person re-identification task. The main idea is to take full advantage of the labels in a batch during training, and to employ PCA to extract discriminative features. Deriving from the classic eigenvalue computation problem in PCA, our method incorporates an extra term in loss function with the purpose of minimizing those relative large eigenvalues. And the derivative with respect to the designed loss can be back-propagated in deep network by stochastic gradient descent (SGD). Experiments show the effectiveness of our algorithm on several re-id datasets.


  1. 1.
    Li, Y., Wu, Z., Karanam, S., Radke, R.J.: Real-world re-identification in an airport camera network. In: International Conference on Distributed Smart Cameras, p. 35 (2014)Google Scholar
  2. 2.
    Camps, O., Gou, M., Hebble, T., Karanam, S., Lehmann, O., Li, Y., Radke, R.J., Wu, Z., Xiong, F.: From the lab to the real world: re-identification in an airport camera network. IEEE Trans. Circ. Syst. Video Technol. 27(3), 540–553 (2017)CrossRefGoogle Scholar
  3. 3.
    Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical Gaussian descriptor for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1363–1372 (2016)Google Scholar
  4. 4.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning, vol. 8, no. 4, pp. 2197–2206 (2015)Google Scholar
  5. 5.
    Ma, B., Su, Y., Jurie, F.: Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis. Comput. 32(67), 379–390 (2014)CrossRefGoogle Scholar
  6. 6.
    Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P.M.: Large scale metric learning from equivalence constraints. In: Computer Vision and Pattern Recognition, pp. 2288–2295 (2012)Google Scholar
  7. 7.
    Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1335–1344 (2016)Google Scholar
  8. 8.
    Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 152–159 (2014)Google Scholar
  9. 9.
    Yi, D., Lei, Z., Liao, S., Li, S.Z.: Deep metric learning for person re-identification. In: International Conference on Pattern Recognition, pp. 34–39 (2014)Google Scholar
  10. 10.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering, pp. 815–823 (2015)Google Scholar
  11. 11.
    Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification (2016)Google Scholar
  12. 12.
    Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48(10), 2993–3003 (2015)CrossRefGoogle Scholar
  13. 13.
    Mclaughlin, N., Rincon, J.M.D., Miller, P.: Recurrent convolutional network for video-based person re-identification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1325–1334, June 2016Google Scholar
  14. 14.
    Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: IEEE International Conference on Multimedia and Expo, vol. 47, no. 9, pp. 11–26 (2016)Google Scholar
  15. 15.
    Ranjan, R., Castillo, C.D., Chellappa, R.: L2-constrained softmax loss for discriminative face verification. CoRR abs/1703.09507 (2017)Google Scholar
  16. 16.
    Zheng, W.S., Li, X., Xiang, T., Liao, S.: Partial person re-identification. In: IEEE International Conference on Computer Vision, pp. 4678–4686 (2015)Google Scholar
  17. 17.
    Zheng, W.S., Gong, S., Xiang, T.: Towards open-world person re-identification by one-shot group-based verification. IEEE Trans. Pattern Anal. Mach. Intell. 38(3), 1 (2016)CrossRefGoogle Scholar
  18. 18.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks (2012)Google Scholar
  19. 19.
    Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. CoRR abs/1703.07737 (2017)Google Scholar
  20. 20.
    Dorfer, M., Kelz, R., Widmer, G.: Deep linear discriminant analysis. In: NBER Chapters, vol. 5 (2015)Google Scholar
  21. 21.
    Liong, V.E., Lu, J., Wang, G.: Face recognition using Deep PCA (2013)Google Scholar
  22. 22.
    Chan, T.H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn., pp. 2133–2143. Academic Press, San Diego (1990)Google Scholar
  24. 24.
    Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., Tian, Q.: MARS: a video benchmark for large-scale person re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 868–884. Springer, Cham (2016). CrossRefGoogle Scholar
  25. 25.
    Li, W., Wang, X.: Locally aligned feature transforms across views. In: Computer Vision and Pattern Recognition, pp. 3594–3601 (2013)Google Scholar
  26. 26.
    Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  27. 27.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking (2007)Google Scholar
  28. 28.
    Baltieri, D., Vezzani, R., Cucchiara, R.: 3DPeS: 3D people dataset for surveillance and forensics. In: Joint ACM Workshop on Human Gesture and Behavior Understanding, pp. 59–64 (2011)Google Scholar
  29. 29.
    Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: Proceedings of the British Machine Vision Conference, BMVC 2009, London, UK, 7–10 September 2009Google Scholar
  30. 30.
    Moon, H., Phillips, P.J.: Computational and performance aspects of PCA-based face-recognition algorithms. Perception 30(3), 303–321 (2001)CrossRefGoogle Scholar
  31. 31.
    Zhang, L., Xiang, T., Gong, S.: Learning a discriminative null space for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1239–1248 (2016)Google Scholar
  32. 32.
    Paisitkriangkrai, S., Shen, C., Hengel, A.V.D.: Learning to rank in person re-identification with metric ensembles, pp. 1846–1855 (2015)Google Scholar
  33. 33.
    Wu, L., Shen, C., Hengel, A.V.D.: Deep linear discriminant analysis on fisher networks: a hybrid architecture for person re-identification. Pattern Recogn. 65, 238–250 (2017)CrossRefGoogle Scholar
  34. 34.
    Varior, R.R., Haloi, M., Wang, G.: Gated siamese convolutional neural network architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 791–808. Springer, Cham (2016). CrossRefGoogle Scholar
  35. 35.
    Bai, S., Bai, X., Tian, Q.: Scalable person re-identification on supervised smoothed manifold (2017)Google Scholar
  36. 36.
    Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Kaixuan Zhang
    • 1
  • Yang Xu
    • 1
  • Li Sun
    • 1
    Email author
  • Song Qiu
    • 1
  • Qingli Li
    • 1
  1. 1.Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina

Personalised recommendations