关键词: 人脸检测, 离散余弦变换, 奇异值分解, 局部二值模式

Abstract: To solve the deficiency of describing face information by single feature, a novel method of face detection based on multiple facial feature extraction is proposed. Wavelet analysis is used to reduce the dimensions of the candidate face getting from skin segmentation, and discrete cosine transform is used to extract the coefficients as frequency domain features. Both the algebra features and the texture features are extracted from discrete cosine transform reconstruction image by singular value decomposition and local binary mode respectively. These extracted features are combined to be a new characteristic vector with much lower dimensions than the original image, while its ability to characterize is still strong because of the comprehensive advantages of three features. Support vector machine is applied to classifying and locating human face. The experimental results show that this method has good detection rate and high robustness.

Key words: face detection, discrete cosine transform, singular value decomposition, local binary mode