基于字典扩展的快速人脸识别算法

Fast face recognition based on dictionary expansion

利用比l1-范数最小化更高效的l2-范数最小化算法,提出了一种在多种人脸数据库上整体更为准确,且比经典基于稀疏表示的人脸分类算法更高效的人脸识别算法。它在传统的训练字典中加入了一个特征矩阵,增大特征信息在字典矩阵中的比重,从而提高识别的准确性。在一系列的实验结果中得出,该人脸识别算法比现有的其他几种典型算法更加准确,而且对噪声和遮挡块的抗干扰性也更强。

This paper proposes an algorithm based on the l2-norm minimization which is much better than the l1-norm minimization.This algorithm is much more accurate on some database and efficient than the traditional sparse representationbased classification.The algorithm adds a feature dictionary into the training dictionary,which can increase the proportion of the feature information and raise the recognition rate.In a series of experiments,it can be found that the method is more accurate in the recognition rate and robust to both pixel corruption and block occlusion than other methods.