Robust feature vector composition for frontal face detection
Seung‐Ik Lee, Won Chul-Ho, Im Sung-Woon, Duk-Gyoo Kim · Journal of the Institute of Electronics Engineers of Korea · 2005
The robust feature vector selection method for the multiple frontal face detection is proposed in this paper. The proposed feature vector for the training and classification are integrated by means, amplitude projections, and its 1D Harr wavelet of the input image. And the statistical modeling is performed both for face and nonface classes. Finally, the estimated probability density functions (PDFs) are applied for the detection of multiple frontal faces in the still image. The proposed method can handle multiple faces, partially occluded faces, and slightly posed-angle faces. And also the proposed method is very effective for low quality face images. Experimental results show that detection rate of the propose method is with three false detections on the testing data, SET3 which have 227 faces in 80 images.