Advertisement

Face Recognition via Compact Fisher Vector

  • Hongjun Wang
  • Weihong Deng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9428)

Abstract

Efficient encoding of facial descriptors remains to be a major topic for face recognition. Among various methods, Fisher vector (FV) representations have shown satisfying performance on most benchmark datasets. However, its representation is huge. In this paper, we present a novel approach to make Fisher vector compact and improves its performance. We utilize handcrafted low-level descriptors as FV do. However, we retain only 1st order statistics of FV, introduce Gaussian block to sparsify FV, alter its formulation, and normalize properly. We evaluate our method on LFW and FERET dataset, and result shows our method effectively compresses Fisher vector and achieves satisfying result at the same time.

Keywords

Fisher vector Face recognition Compact descriptor Discriminant descriptor 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Perronnin, F., Liu, Y., Sánchez, J., Poirier, H.: Large-scale Image Retrieval with Compressed Fisher Vectors. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3384–3391. IEEE (2010)Google Scholar
  2. 2.
    Everts, I., van Gemert, J., Mensink, T., Gevers, T.: Robustifying Descriptor Instability Using Fisher Vectors. Transaction on Image Processing (TIP) 23(12), 5698–5706 (2014). IEEECrossRefGoogle Scholar
  3. 3.
    Vedaldi, A., Fulkerson, B.: VLFeat: An Open and Portable Library of Computer Vision Algorithms. In: Proceedings of the International Conference on Multimedia, pp. 1469–1472. ACM (2010)Google Scholar
  4. 4.
    Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The Devil is in the Details: an Evaluation of Recent Feature Encoding Methods. In: British Machine Vision Conference (2011)Google Scholar
  5. 5.
    Huang, Y., Wu, Z., Wang, L., Tan, T.: Feature Coding in Image Classification: a Comprehensive Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(3), 493–506 (2014). IEEECrossRefGoogle Scholar
  6. 6.
    Van Gemert, J.C., Veenman, C.J., Smeulders, A.W., Geusebroek, J.M.: Visual Word Ambiguity. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(7), 1271–1283 (2010). IEEECrossRefGoogle Scholar
  7. 7.
    Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image Classification with the Fisher Vector: Theory and Practice. International Journal of Computer Vision 105(3), 222–245 (2013). ACMMathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating Local Descriptors into a Compact Image Representation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3304–3311. IEEE (2010)Google Scholar
  9. 9.
    Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C.: Aggregating Local Image Descriptors into Compact Codes. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(9), 1704–1716 (2012). IEEECrossRefGoogle Scholar
  10. 10.
    Jegou, H., Douze, M., Schmid, C.: Product Quantization for Nearest Neighbor Search. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1), 117–128 (2011). IEEECrossRefGoogle Scholar
  11. 11.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169–2178. IEEE (2006)Google Scholar
  13. 13.
    Krapac, J., Verbeek, J., Jurie, F.: Modeling Spatial Layout with Fisher Vectors for Image Categorization. In: IEEE International Conference on Computer Vision (ICCV), pp. 1487–1494. IEEE (2011)Google Scholar
  14. 14.
    Sánchez, J., Perronnin, F., De Campos, T.: Modeling the Spatial Layout of Images Beyond Spatial Pyramids. Pattern Recognition Letters 33(16), 2216–2223 (2012). ElsevierCrossRefGoogle Scholar
  15. 15.
    Simonyan, K., Parkhi, O.M.: Fisher Vector Faces in the Wild. In: British Machine Vision Conference (2013)Google Scholar
  16. 16.
    Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic Elastic Matching for Pose Variant Face Verification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3499–3506. IEEE (2013)Google Scholar
  17. 17.
    Arandjelovic, R., Zisserman, A.: All about VLAD. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1578–1585. IEEE (2013)Google Scholar
  18. 18.
    Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the Devil in the Details: Delving Deep into Convolutional Nets. In: British Machine Vision Conference (2014)Google Scholar
  19. 19.
    Garg, V., Chandra, S., Jawahar, C.V.: Sparse Discriminative Fisher Vectors in Visual Classification. In: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, p. 55. ACM (2012)Google Scholar
  20. 20.
    Delhumeau, J., Gosselin, P.H., Jégou, H., Pérez, P.: Revisiting the VLAD Image Representation. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 653–656. ACM (2013)Google Scholar
  21. 21.
    Jégou, H., Douze, M., Schmid, C.: On the Burstiness of Visual Elements. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1169–1176. IEEE (2009)Google Scholar
  22. 22.
    Wu, Y.H., Ku, W.L., Peng, W.H., Chou, H.C.: Global Image Representation using Locality-constrained Linear Coding for Large-scale Image Retrieval. In: 2014 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 766–769. IEEE (2014)Google Scholar
  23. 23.
    Arandjelovic, R., Zisserman, A.: Three Things Everyone Should Know to Improve Object Retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2911–2918. IEEE (2012)Google Scholar
  24. 24.
    Huang, C.: Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval. In: CoRR (2012). http://arxiv.org/abs/1212.6094
  25. 25.
    Prince, S., Li, P.: Probabilistic Models for Inference about Identity. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(1), 144–157 (2012). IEEECrossRefGoogle Scholar
  26. 26.
    Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of Dimensionality: High-dimensional Feature and its Efficient Compression for Face Verification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3025–3032. IEEE (2013)Google Scholar
  27. 27.
    Guillaumin, M., Verbeek, J.J., Schmid, C.: Is That You? Metric Learning Approaches for Face Identification. In: IEEE International Conference on Computer Vision (ICCV), pp. 498–505. IEEE (2009)Google Scholar
  28. 28.
    Chen, D., Cao, X., Wang, L., Wen, F., Sun, J.: Bayesian face revisited: a joint formulation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 566–579. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  29. 29.
    Dai, X.: A Convolutional Neural Network Approach for Face Identification. In: Proceedings of the 30th International Conference on Machine Learning, pp. 98–113. IEEE (2013)Google Scholar
  30. 30.
    Sun, Y., Wang, X., Tang, X.: Hybrid Deep Learning for Face Verification. In: IEEE International Conference on Computer Vision (ICCV), pp. 1489–1496. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationBeijingPeople’s Republic of China

Personalised recommendations