基于Gabor滤波器的快速人脸识别算法

New fast face recognition algorithm based on Gabor filter

针对传统人脸识别方法中所提取特征维数高、计算量大等缺点,提出一种新的正面人脸识别算法。新算法融合了半边人脸识别方法、Gabor滤波器、基于互信息判据的Gabor特征筛选来进行人脸识别。新算法将人脸图像分为左右两个部分,计算并比较人脸图像左右半边脸的熵,选取熵值较大的半边人脸图像进行Gabor特征提取。利用二值分类器判别单个Gabor特征的分类能力,选取分类能力较强的特征(最具判决力的特征)。再利用互信息判据对Gabor特征进行第二次筛选,以减小特征之间的冗余度。最后利用最近邻判别器来进行人脸识别。实验结果表明,新算法的识别率优于传统半边脸识别方法,识别速度也优于传统的利用Gabor滤波器进行特征提取的方法。

Concerning the disadvantage of traditional face recognition algorithm,such as high dimension of extracted feature,a great deal of computation,a fast face recognition algorithm was proposed.The algorithm integrated the half face recognition scheme,Gabor filter,Gabor features selecting method based on mutual information,and the nearest neighbor method for frontal face recognition.The face images in training set and testing set were divided into the left half and the right half,one half of the face images was chosen by entropy maximum.The features of the face images were extracted by Gabor filter.Then the rank of discriminating capabilities of features can be estimated by evaluating the classification error on intra-set and extra-set based on weak classifier built by single feature.The Gabor features with small errors were selected.And at the same time,the mutual information between the selected features was examined.The nearest neighbor method was used to recognize the frontal face.The experimental results show that the proposed method has higher accuracy than the traditional half face recognition algorithm,and is of lower computational complexity than the traditional Gabor filter algorithm.