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Multiple feature subspaces analysis for single sample per person face recognition

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Abstract

Collecting samples is one of the main difficulties for face recognition, for example, in most of the real-world applications such as law enhancement, e-passport, and ID card identification, it is customary to collect a single sample per person (SSPP). Unfortunately, in such SSPP scenario, many presented face recognition methods suffer serious performance drop or fail due to their inability to learn the discriminative information of a person from a single sample. To address the SSPP problem, in this paper, we propose a multiple feature subspaces analysis (MFSA) approach, which takes advantage of facial symmetry. First, we divide each enrolled face into two halves about the bilateral symmetry axis and further partition every half into several local face patches. Second, we cluster all the patches into multiple groups according to their locations at the half face and formulate SSPP as a MFSA problem by learning a feature subspace for each group, so that the confusion between inter-class and intra-class variations of face patches is removed and more discriminative features can be extracted from each subspace. To recognize a target person, a k-NN classifier is employed in each subspace to predict the label of a face patch and majority voting strategy is used to identify the unlabeled subject. Compared with the state-of-the-art methods, MFSA is effortless and efficient in implementing, but achieves either better or competitive performance when recognizing face images taken in both constrained and unconstrained environment.

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Authors and Affiliations

  1. Institute of Systems Engineering, Southeast University, Nanjing, 211096, China

    Yongjie Chu & Lindu Zhao

  2. Department of Computer Science and Engineering, University of Nevada, Reno, NV, 89557, USA

    Yongjie Chu & Touqeer Ahmad

Authors
  1. Yongjie Chu
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  2. Lindu Zhao
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Correspondence to Lindu Zhao.

Additional information

This work was partially supported by the National Natural Science Foundation of China (No. 71390333), the National Key Technology R&D Program of China during the 12th Five-Year Plan Period (No. 2013BAD19B05), the Special Fund for Basic Research in Central University, i.e., Fund for Graduate Research Project of Jiangsu Province (KYZZ15_0072), and the Study Abroad Program for Graduate Studies by China Scholarship Council.

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Chu, Y., Zhao, L. & Ahmad, T. Multiple feature subspaces analysis for single sample per person face recognition. Vis Comput 35, 239–256 (2019). https://doi.org/10.1007/s00371-017-1468-4

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