人脸识别中一种新的Gabor特征提取方法

为了有效利用Gabor特征进行人脸识别，提出一种新的Gabor特征提取方法．首先利用类别可分离性判据评价Gabor展开系数的分类能力，选择最有利于识别的Gabor展开系数构造新的Gabor特征、然后对人脸不同局部位置处采样点的分类能力进行评价，选择分类能力最强的位置提取特征点、最后就新的Gabor特征对光照和表情变化的适应性进行测试．实验结果表明，新的特征提取方法能够在小样本条件下有效提高识别率，减少特征数量，并对环境变化具有一定的适应能力．

A New Gabor Feature Extraction Method for Face Recognition

To utilize the Gabor feature for efficient face re cognition, a new Gabor feature extraction method was proposed. Firstly, a criterion with discriminating power was used to evaluate the classification ability of the Gabor coefficients, and the Gabor coefficients most favorable to the recognition were selected to construct new Gabor features. Then, the classification abilities of different local positions in the face were evaluated, and feature points were chosen at the positions with the strongest discriminating power. Finally, the adaptability of the new Gabor feature to illumination and expression was tested. Experiment results show that, with small samples, the new feature extraction method can effectively increase the recognition rate, reduce the number of features, and is of certain adaptability to the environments.