基于梯度图及Hausdorff距离的人脸识别算法

Face Recognition Using Gradient Map and Hausdorff Distance Measure

本文提出一种基于梯度图及PHD(Partial Hausdorff Distance)距离的人脸识别算法。首先,为了使识别独立于光照变化,所有图像均转换为梯度图,其次,采用Hausdorff距离进行图像的匹配,实验结果显示该方法适用于人脸识别,且距离计算对于光照,及较小的姿态、表情变化具有一定的鲁棒性。最后,实验采用AR及FERET人脸数据库,并与EM(Edge Map)与LEM(Line segment Edge Map)算法进行比较。

A gradient- based face recognition method using Partial Hausdorff Distance （PHD） measure is proposed in this paper. First, in order to achieve a performance independent of lighting conditions, the image is transformed into a Gradient Map （GM）. And then, Hausdorff distance measure is introduced to calculate the dissimilarity between two Gradient Maps. The experimental data show that the measure is suitable for face recognition. As we can see later, this distance measure is robust to lighting variations, slight pose differences and expression changes in face images. At last, recognition accuracy is given tested on AR and FERET databases, and comparisons with Edge Map （EM） and Line segment Edge Map （LEM） approaches are also presented.