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Face Recognition Using Sparse Approximated Nearest Points between Image Sets
Oct. 2012 (vol. 34 no. 10)
pp. 1992-2004
Yiqun Hu, The University of Western Australia, Crawley
Ajmal S. Mian, The University of Western Australia, Crawley
Robyn Owens, The University of Western Australia, Crawley
We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and their affine hull model. The model accounts for unseen appearances in the form of affine combinations of sample images. To calculate the between-set distance, we introduce the Sparse Approximated Nearest Point (SANP). SANPs are the nearest points of two image sets such that each point can be sparsely approximated by the image samples of its respective set. This novel sparse formulation enforces sparsity on the sample coefficients and jointly optimizes the nearest points as well as their sparse approximations. Unlike standard sparse coding, the data to be sparsely approximated are not fixed. A convex formulation is proposed to find the optimal SANPs between two sets and the accelerated proximal gradient method is adapted to efficiently solve this optimization. We also derive the kernel extension of the SANP and propose an algorithm for dynamically tuning the RBF kernel parameter while matching each pair of image sets. Comprehensive experiments on the UCSD/Honda, CMU MoBo, and YouTube Celebrities face datasets show that our method consistently outperforms the state of the art.
Index Terms:
Approximation methods,Optimization,Data models,Vectors,Hidden Markov models,Adaptation models,convex optimization.,Image set classification,face recognition,sparse modeling
Citation:
Yiqun Hu, Ajmal S. Mian, Robyn Owens, "Face Recognition Using Sparse Approximated Nearest Points between Image Sets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 10, pp. 1992-2004, Oct. 2012, doi:10.1109/TPAMI.2011.283
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