Face detection based on multi-block LBP representation

  • Authors:

  • Lun Zhang;Rufeng Chu;Shiming Xiang;Shengcai Liao;Stan Z. Li

  • Affiliations:

  • Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
  • Year:
  • 2007

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Abstract

Effective and real-time face detection has been made possible by using the method of rectangle Haar-like features with AdaBoost learning since Viola and Jones' work [12]. In this paper, we present the use of a new set of distinctive rectangle features, called Multi-block Local Binary Patterns (MB-LBP), for face detection. The MB-LBP encodes rectangular regions' intensities by local binary pattern operator, and the resulting binary patterns can describe diverse local structures of images. Based on the MB-LBP features, a boosting-based learning method is developed to achieve the goal of face detection. To deal with the non-metric feature value of MB-LBP features, the boosting algorithm uses multibranch regression tree as its weak classifiers. The experiments show the weak classifiers based on MB-LBP are more discriminative than Haar-like features and original LBP features. Given the same number of features, the proposed face detector illustrates 15% higher correct rate at a given false alarm rate of 0.001 than haar-like feature and 8% higher than original LBP feature. This indicates that MB-LBP features can capture more information about the image structure and show more distinctive performance than traditional haar-like features, which simply measure the differences between rectangles. Another advantage of MB-LBP feature is its smaller feature set, this makes much less training time.