关键词: 人脸识别, 梯度脸, 主成分分析, 单样本, 最近邻分类器

Abstract: To overcome the limitations of traditional face recognition methods for single sample, an improved gradient face algorithm named Orthogonal Gradient Binary Pattern (OGBP), which is robust to variations of illumination, face expression and posture, was proposed. Firstly, the features of the image samples were extracted by orthogonal gradient binary pattern. Then the feature vectors of each direction were concatenated into the general feature vector for face recognition. Finally the Principle Component Analysis (PCA) method was used to reduce dimensions and the nearest neighbor classifier was used for face image classification and recognition. Experimental results on YALE and AR face database indicate that the proposed method is simple, effective and better than the original gradient face algorithm, and also has better performance in face description for single sample.

Key words: face recognition, gradient face, Principle Component Analysis(PCA), single sample, nearest neighbor classifier