Image Description with Local Patterns : An Application to Face Recognition

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著者

    • ZHOU Wei
    • the Graduate School of Information, Production and Systems, Waseda University
    • AHRARY Alireza
    • the Department of Human and Computer Intelligence, Nagasaki Institute of Applied Science
    • KAMATA Sei-ichiro
    • the Graduate School of Information, Production and Systems, Waseda University

抄録

In this paper, we propose a novel approach for presenting the local features of digital image using 1D Local Patterns by Multi-Scans (1DLPMS). We also consider the extentions and simplifications of the proposed approach into facial images analysis. The proposed approach consists of three steps. At the first step, the gray values of pixels in image are represented as a vector giving the local neighborhood intensity distrubutions of the pixels. Then, multi-scans are applied to capture different spatial information on the image with advantage of less computation than other traditional ways, such as Local Binary Patterns (LBP). The second step is encoding the local features based on different encoding rules using 1D local patterns. This transformation is expected to be less sensitive to illumination variations besides preserving the appearance of images embedded in the original gray scale. At the final step, Grouped 1D Local Patterns by Multi-Scans (G1DLPMS) is applied to make the proposed approach computationally simpler and easy to extend. Next, we further formulate boosted algorithm to extract the most discriminant local features. The evaluated results demonstrate that the proposed approach outperforms the conventional approaches in terms of accuracy in applications of face recognition, gender estimation and facial expression.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 95(5), 1494-1505, 2012-05-01

    The Institute of Electronics, Information and Communication Engineers

参考文献:  27件中 1-27件 を表示

被引用文献:  2件中 1-2件 を表示

各種コード

  • NII論文ID(NAID)
    10030943272
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168532
  • データ提供元
    CJP書誌  CJP引用  J-STAGE 
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