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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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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