CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.01 - Jan.
Issue No.01 - Jan. (2013 vol.35)
Jiwen Lu , Adv. Digital Sci. Center, Singapore, Singapore
Yap-Peng Tan , Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
Gang Wang , Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.70
Conventional appearance-based face recognition methods usually assume that there are multiple samples per person (MSPP) available for discriminative feature extraction during the training phase. In many practical face recognition applications such as law enhancement, e-passport, and ID card identification, this assumption, however, may not hold as there is only a single sample per person (SSPP) enrolled or recorded in these systems. Many popular face recognition methods fail to work well in this scenario because there are not enough samples for discriminant learning. To address this problem, we propose in this paper a novel discriminative multimanifold analysis (DMMA) method by learning discriminative features from image patches. First, we partition each enrolled face image into several nonoverlapping patches to form an image set for each sample per person. Then, we formulate the SSPP face recognition as a manifold-manifold matching problem and learn multiple DMMA feature spaces to maximize the manifold margins of different persons. Finally, we present a reconstruction-based manifold-manifold distance to identify the unlabeled subjects. Experimental results on three widely used face databases are presented to demonstrate the efficacy of the proposed approach.
Manifolds, Face recognition, Face, Training, Feature extraction, Semantics, Educational institutions,single training sample per person, Face recognition, manifold learning, subspace learning
Jiwen Lu, Yap-Peng Tan, Gang Wang, "Discriminative Multimanifold Analysis for Face Recognition from a Single Training Sample per Person", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 1, pp. 39-51, Jan. 2013, doi:10.1109/TPAMI.2012.70