Learning Discriminative Multi-scale and Multi-position LBP Features for Face Detection Based on Ada-LDA
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 Violas 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.
Kwang Ho An So Hee Park Yun Su Chung Ki Young Moon Myung Jin Chung
Electrical Engineering and Computer Science Department,Korea Advanced Institute of Science and Techn Knowledge-based Information Security & Safety Research Department,Electronics and Telecommunications
国际会议
2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)
桂林
英文
1117-1122
2009-12-19（万方平台首次上网日期，不代表论文的发表时间）