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Biologically Inspired Tensor Features

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Abstract

According to the research results reported in the past decades, it is well acknowledged that face recognition is not a trivial task. With the development of electronic devices, we are gradually revealing the secret of object recognition in the primate’s visual cortex. Therefore, it is time to reconsider face recognition by using biologically inspired features. In this paper, we represent face images by utilizing the C1 units, which correspond to complex cells in the visual cortex, and pool over S1 units by using a maximum operation to reserve only the maximum response of each local area of S1 units. The new representation is termed C1 Face. Because C1 Face is naturally a third-order tensor (or a three dimensional array), we propose three-way discriminative locality alignment (TWDLA), an extension of the discriminative locality alignment, which is a top-level discriminate manifold learning-based subspace learning algorithm. TWDLA has the following advantages: (1) it takes third-order tensors as input directly so the structure information can be well preserved; (2) it models the local geometry over every modality of the input tensors so the spatial relations of input tensors within a class can be preserved; (3) it maximizes the margin between a tensor and tensors from other classes over each modality so it performs well for recognition tasks and (4) it has no under sampling problem. Extensive experiments on YALE and FERET datasets show (1) the proposed C1Face representation can better represent face images than raw pixels and (2) TWDLA can duly preserve both the local geometry and the discriminative information over every modality for recognition.

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Acknowledgments

This project was partially supported by the Nanyang Technological University Start-Up Grant (under project number M58020010), 100 Talents Program of The Chinese Academy of Sciences and K. C. WONG Education Foundation Award of the Chinese Academy of Sciences.

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Authors and Affiliations

  1. Nanyang Technological University, Singapore, 639798, Singapore

    Yang Mu & Dacheng Tao

  2. State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, Shaanxi, China

    Xuelong Li

  3. Department of Computer Science, Royal Holloway, University of London, Surrey, TW20 0EX, UK

    Fionn Murtagh

Authors
  1. Yang Mu
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  2. Dacheng Tao
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  3. Xuelong Li
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  4. Fionn Murtagh
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Correspondence to Dacheng Tao.

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Mu, Y., Tao, D., Li, X. et al. Biologically Inspired Tensor Features. Cogn Comput 1, 327–341 (2009). https://doi.org/10.1007/s12559-009-9028-5

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  • DOI: https://doi.org/10.1007/s12559-009-9028-5

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