基于中心对称梯度幅值相位模式的单样本人脸识别

针对传统人脸识别算法在单训练样本情况下识别 效果不佳的问题,提出一种基于中心对称梯度幅值 相位模式(CSGMP)的单样本人脸识别算法。首先,提取人脸图像的梯度幅值和相位信息；然 后,用一种新 的中心对称局部方向模式(CSLDP)算子对梯度幅值进行编码,再将梯度相位量化到8个区间 进行编码,将 二者融合形成人脸图像的CSGMP特征；最后,分块统计直方图特征信息,将所有块的直方图 串联后作为 人脸图像的特征向量,利用最近邻分类器分类识别。在YALE和AR人脸库上进行测试的结果表 明,本文所提方 法简单有效,对光照变化、表情变化和部分遮挡等环境下单样本人脸识别具有较好的效果。

Face recognition based on center-symmetric gradient magnitude and phase pattern

To overcome the limitations of traditional face recognition methods fo r single sample,a novel method of face recognition based on center-symmetric gradient magnitude and phase patt ern (CSGMP) is proposed. Firstly,gradient magnitude maps and phase maps of a face image are calculated. Secondly,a new operator named center-symmetric local directional pattern (CSLDP) is proposed to encode the g radient magnitude,and gradient phase is quantized into eight regions,then the proposed CSGMP is the combinat ion of the binary codes of phase and CSLDP of magnitude.Finally,CSGMP feature maps are divided into seve ral blocks,and the concatenated histogram calculated over all blocks is utilized as the feature des criptor of face recognition.The recognition is performed by using the nearest neighbor classifier.Experimental results on YALE and AR face databases validate that the CSGMP algorithm is an outstanding method for single sample face recognition under different illumination conditions,different facial expression conditions and par tial occlusion conditions.