关键词: 边缘向量模式（EVP）, 多核支持向量机, 伽柏, 局部二值模式（LBP）, 实时人脸识别

Abstract: This paper proposes a novel descriptor EVP（Edge Vector Pattern）, and the combination of common classifiers is composed for face recognition, to compare with other known methods. EVP is characterized not only by using gradient values instead of the pixel intensity values to describe local object appearance and shape, but also describes the relationship between neighborhood information. Based on the FERET face image database, when using the nearest neighbor classifier, EVP is superior to other methods. Based on the LFW database, and the combination of MKL-SVM, the result is impressive. More commendable is that EVP is roughly 20 times faster than methods based on Gabor, while Gabor method is now considered as one of the best methods.

Key words: Edge Vector Pattern（EVP）, Multiple Kernel Learning-Support Vector Machine（MKL-SVM）, Gabor, Local Binary Pattern（LBP）, real time face recognition