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Image Generation Using Bidirectional Integral Features for Face Recognition with a Single Sample per Person

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  • Yonggeol Lee
  • Minsik Lee
  • Sang-Il Choi

Abstract

In face recognition, most appearance-based methods require several images of each person to construct the feature space for recognition. However, in the real world it is difficult to collect multiple images per person, and in many cases there is only a single sample per person (SSPP). In this paper, we propose a method to generate new images with various illuminations from a single image taken under frontal illumination. Motivated by the integral image, which was developed for face detection, we extract the bidirectional integral feature (BIF) to obtain the characteristics of the illumination condition at the time of the picture being taken. The experimental results for various face databases show that the proposed method results in improved recognition performance under illumination variation.

Suggested Citation

  • Yonggeol Lee & Minsik Lee & Sang-Il Choi, 2015. "Image Generation Using Bidirectional Integral Features for Face Recognition with a Single Sample per Person," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-13, September.
    • Handle: RePEc:plo:pone00:0138859
    DOI: 10.1371/journal.pone.0138859
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    References listed on IDEAS

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    1. Qin Li & Hua Jing Wang & Jane You & Zhao Ming Li & Jin Xue Li, 2013. "Enlarge the Training Set Based on Inter-Class Relationship for Face Recognition from One Image per Person," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-9, July.
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