Learning discriminative multi-scale and multi-position LBP features for face detection based on Ada-LDA

Authors:
Kwang Ho An;So Hee Park;Yun Su Chung;Ki Young Moon;Myung Jin Chung
Affiliations:
Electrical Engineering and Computer Science Department, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea;Knowledge-based Information Security & Safety Research Department, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea;Knowledge-based Information Security & Safety Research Department, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea;Knowledge-based Information Security & Safety Research Department, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea;Electrical Engineering and Computer Science Department, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
Venue:
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Year:
2009

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Abstract

This paper presents a novel approach for face detection, which is based on the discriminative MspLBP features selected by a boosting technique called the Ada-LDA method. By scanning the face image with a scalable sub-window, many sub-regions are obtained from which the MspLBP features are extracted to describe the local structures of a face image. From a large pool of the MspLBP features within the face image, the most discriminative MspLBP features that are trained by two alternative LDA methods depending on the singularity of the within-class scatter matrix of the training samples are selected under the framework of AdaBoost. To verify the feasibility of our face detector, we performed extensive experiments on the MIT-CBCL and MIT+CMU face test sets. Given the same number of features, the proposed face detector shows a detection rate of 25% higher than the well-known Viola's detector at a given false positive rate of 10%. Challenging experimental results prove that our face detector can show promising detection performance with only a small number of the discriminative MspLBP features. It can also provide real-time performance. Our face detector can operate at over 16 frames per second.