상세정보
학술대회
Learning Discriminative Multi-scale and Multi-position LBP Features for Face Detection Based on Ada-LDA
- 발행일
- 200912
- 출처
- International Conference on Robotics and Biomimetics (ROBIO) 2009, pp.1117-1122
- 초록
- 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. © 2009 IEEE.
- KSP 제안 키워드
- Face Image, Face detection, False Positive Rate, Frames per second(FPS), LBP features, MIT-CBCL, Multi-scale, Novel approach, Real-time performance, Training samples, Within-class scatter