Abstract
Face verification is the task of deciding by analyzing face images, whether a person is who he/she claims to be. This is very challenging due to image variations in lighting, pose, facial expression, and age. The task boils down to computing the distance between two face vectors. As such, appropriate distance metrics are essential for face verification accuracy. In this paper we propose a new method, named the Cosine Similarity Metric Learning (CSML) for learning a distance metric for facial verification. The use of cosine similarity in our method leads to an effective learning algorithm which can improve the generalization ability of any given metric. Our method is tested on the state-of-the-art dataset, the Labeled Faces in the Wild (LFW), and has achieved the highest accuracy in the literature.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Phillips, P., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16, 295–306 (1998)
Shan, S., Zhang, W., Su, Y., Chen, X., Gao, W., Frjdl, I., Cas, B.: Ensemble of Piecewise FDA Based on Spatial Histograms of Local (Gabor) Binary Patterns for Face Recognition. In: Proceedings of the 18th International Conference on Pattern Recognition, pp. 606–609 (2006)
Hieu, N., Bai, L., Shen, L.: Local gabor binary pattern whitened pca: A novel approach for face recognition from single image per person. In: Proceedings of the 3rd IAPR/IEEE International Conference on Biometrics (2009)
Shen, L., Bai, L.: MutualBoost learning for selecting Gabor features for face recognition. Pattern Recognition Letters 27, 1758–1767 (2006)
Shen, L., Bai, L., Fairhurst, M.: Gabor wavelets and general discriminant analysis for face identification and verification. Image and Vision Computing 27, 1758–1767 (2006)
Nguyen, H.V., Bai, L.: Compact binary patterns (cbp) with multiple patch classifiers for fast and accurate face recognition. In: CompIMAGE, pp. 187–198 (2010)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst (2007)
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? metric learning approaches for face identification. In: International Conference on Computer Vision, pp. 498–505 (2009)
Taigman, Y., Wolf, L., Hassner, T.: Multiple one-shots for utilizing class label information. In: The British Machine Vision Conference, BMVC (2009)
Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighborhood component analysis. In: NIPS
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning, with application to clustering with side-information. In: Advances in Neural Information Processing Systems 15, vol. 15, pp. 505–512 (2003)
Weinberger, K., Blitzer, J., Saul, L.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems 18, pp. 1473–1480 (2006)
Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: ICML 2007: Proceedings of the 24th International Conference on Machine Learning, pp. 209–216. ACM, New York (2007)
Wolf, L., Hassner, T., Taigman, Y.: Similarity scores based on background samples. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5995, pp. 88–97. Springer, Heidelberg (2010)
Ahonen, T., Hadid, A., Pietikainen, M.: Face Recognition with Local Binary Patterns. In: Guessarian, I. (ed.) LITP 1990. LNCS, vol. 469, pp. 469–481. Springer, Heidelberg (1990)
Daugman, J.: Complete Discrete 2D Gabor Transforms by Neural Networks for Image Analysis and Compression. IEEE Trans. Acoust.Speech Signal Process. 36 (1988)
Shan, S., Gao, W., Chang, Y., Cao, B., Yang, P.: Review the strength of Gabor features for face recognition from the angle of its robustness to mis-alignment. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 1 (2004)
Tan, X., Triggs, B.: Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 235–249. Springer, Heidelberg (2007)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition. In: Proc. ICCV, pp. 786–791 (2005)
Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: Real-Life Images Workshop at the European Conference on Computer Vision, ECCV (2008)
Deng, W., Hu, J., Guo, J.: Gabor-Eigen-Whiten-Cosine: A Robust Scheme for Face Recognition. In: Zhao, W., Gong, S., Tang, X. (eds.) AMFG 2005. LNCS, vol. 3723, pp. 336–349. Springer, Heidelberg (2005)
http://vis-www.cs.umass.edu/lfw/results.html: (LFW benchmark results)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, H.V., Bai, L. (2011). Cosine Similarity Metric Learning for Face Verification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-19309-5_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19308-8
Online ISBN: 978-3-642-19309-5
eBook Packages: Computer ScienceComputer Science (R0)