LAUSR

Face detection is an important part of face image processing. In many cases, face images have occlusion problems. In this paper, the POOA (positioning the optimal occlusion area) algorithm is proposed for the problem of occlusion face detection. After obtaining the data of the saliency detection processing, firstly, the algorithm computes an average gray value according to the face image, and multiplies the appropriate coefficient as a threshold to obtain a binary image. Then, using the idea of the Haar feature, the two features of “large rectangle” and “large T shape” are used for retrieval, and the occlusion region of the face is obtained by combining the binary images. Finally, a robust principal component analysis (PCA) method is used to obtain the best projection of the occlusion face, and the face occlusion area is filled. The algorithm proposed in this paper is fast. The Adaboost method has achieved good results in terms of occlusion area, size and shape, and the detection precision has also been improved to varying degrees.