Abstract
Face verification in an uncontrolled environment is a challenging task due to the possibility of large variations in pose, illumination, expression, occlusion, age, scale, and misalignment. To account for these intra-personal settings, this paper proposes a sparsity sharing embedding (SSE) method for face verification that takes into account a pair of input faces under different settings. The proposed SSE method measures the distance between two input faces \({\mathbf x}_A\) and \({\mathbf x}_B\) under intra-personal settings s A and s B in two steps: 1) in the association step, \({\mathbf x}_A\) and \({\mathbf x}_B\) is represented in terms of a reconstructive weight vector and identity under settings s A and s B , respectively, from the generic identity dataset; 2) in the prediction step, the associated faces are replaced by embedding vectors that conserve their identity but are embedded to preserve the inter-personal structures of the intra-personal settings. Experiments on a MultiPIE dataset show that the SSE method performs better than the AP model in terms of the verification rate.
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References
Blanz, V., Vetter, T.: Face recognition based on fitting a 3d morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1063–1074 (2003)
Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. In: Computer Vision and Pattern Recognition (2012)
Chang, C., Lin, C.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2, 27 (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition (2005)
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? metric learning approaches for face identification. In: International Conference on Computer Vision (2009)
He, X., Cai, D., Yan, S., Zhang, H.: Neighborhood preserving embedding. In: International Conference on Computer Vision (2005)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 328–340 (2005)
Kumar, N., Berg, A., Belhumeur, P., Nayar, S.: Attribute and simile classifiers for face verification. In: International Conference on Computer Vision (2009)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Nguyen, H.V., Bai, L.: Cosine Similarity Metric Learning for Face Verification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part II. LNCS, vol. 6493, pp. 709–720. Springer, Heidelberg (2011)
Niyogi, X.: Locality preserving projections. In: Advances in Neural Information Processing System (2004)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Qiao, L., Chen, S., Tan, X.: Sparsity preserving projections with applications to face recognition. Pattern Recognition 43, 331–341 (2010)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Schroff, F., Treibitz, T., Kriegman, D., Belongie, S.: Pose, illumination and expression invariant pairwise face-similarity measure via doppelgänger list comparison. In: International Conference on Computer Vision (2011)
Su, Y., Shan, S., Chen, X., Gao, W.: Adaptive generic learning for face recognition from a single sample per person. In: Computer Vision and Pattern Recognition (2010)
Tang, X., Wang, X.: Face sketch recognition. IEEE Transactions on Circuits and Systems for Video Technology 14, 50–57 (2004)
Wolf, L., Hassner, T., Taigman, Y.: Similarity Scores Based on Background Samples. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp. 88–97. Springer, Heidelberg (2010)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 210–227 (2009)
Yin, Q., Tang, X., Sun, J.: An associate-predict model for face recognition. In: Computer Vision and Pattern Recognition (2011)
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Lee, D., Park, H., Chung, J., Song, Y., Yoo, C.D. (2013). Sparsity Sharing Embedding for Face Verification. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_49
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DOI: https://doi.org/10.1007/978-3-642-37444-9_49
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