关键词: 人脸识别, 单样本, 相位差分, 边缘梯度, 最近邻分类器

Abstract: To overcome the limitations of traditional face recognition algorithm under the single training sample, a fusion of local direction gradient amplitude and phase difference is proposed. The method firstly extracts the gradient amplitude and phase of the image. 8 edge gradient values of each sub-neighborhood are gained by convolving the gradient amplitude with 8 Kirsch masks respectively. And secondly the local difference on the phase is extracted. Then, the LDGMPD just utilizes the direction of the largest value of the edge gradient and local difference, these two directions are encoded into a double digit octal number to produce the LDGMPD code values, it selects the structure contrast information to carry out the weighted processing on face image sub-block of each LDGMPD and extracts the LDGMPD histogram features of face image. Finally, nearest neighbor classifier is used to classify the faces. Experimental results on AR and CAS-PEAL-R1 face databases validate that LDGMPD algorithm is an outstanding method for single sample face recognition.

Key words: face recognition, single training sample, difference on phase, edge gradient, nearest neighbor classifier