人脸识别中基于互信息的特征优选

人脸识别领域中常用Gabor小波系数表示人脸特征.然而,提取的人脸Gabor特征是高维数据,不可避免存在冗余和随机噪声的干扰.为了有效利用Gabor特征进行人脸识别,提出一种新的Gabor特征选取方法.首先计算训练集上的任两张人脸图像的Gabor特征差,生成类内空阃和类外空间.用单个Gabor特征训练筒单两值分类器,以其在类内空间和类外空间的分类错误率作为判据评价该Gabor特征的分类能力.在选取分类错误低的特征的同时还要再评估候选特征与已选特征间的互信息,这样优选出具有无冗余、低误差率的特征.最后对这些优选的Gabor特征进行主成分分析和线性判别分析完成人脸识别.在CAS-PEAL大型人脸数据库上的实验结果表明,所提出的方法不但可大大降低Gabor特征的维数,而且还有效提高了识别精度.

Optimal feature selection based on mutual information for face recognition

Gabor face representation has been getting popular in face recognition applications. However, it also suffers from the high dimensional data containing diverse redundancy and different random noises. To utilize the Gabor feature for efficient face recognition, a new Gabor feature selection method is proposed. Firstly, the Gabor feature differences between every two face images within a training data set are calculated and grouped into two categories: intra-individual set and extra-individual set. 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 candidate feature and the selected features was examined. As a result, the non-effective features carrying information already captured by the selected features will be excluded.The features thus selected are both accurate and non-redundant. Finally, the selected Gabor features were classified by PCA and LDA for final face recognition. The experiments on CAS-PEAL large-scale Chinese face database show that the proposed method can greatly reduce the dimensionality of Gabor features and effectively increase the recognition accuracy.