局部匹配的人脸识别方法

从人类认知方式出发，提出了一种基于统计学习的局部匹配人脸识别方法。该方法将人脸图像划分成若干小块，各个子块中包含不同的人脸形状特征，而不同的子块则描述了人脸主要部件之间的相对位置关系，然后根据各个子块鉴别能力的差异，将每个子块看成一弱分类器，利用Adaboost学习算法组成一个强分类器，提高最终的分类效果。实验结果表明该方法可以有效提高人脸的识别准确率并对人脸的表情和光照具有较好的鲁棒性。

Face recognition method with local matching

From human cognition, a face recognition method with local matching based on statistical learning is proposed. The image is divided into several subimages and each subimage is considered as a weak classifier. The Adaboost learning algorithm is used to train the weak classifiers and construct a strong classifier. As a result, all subimages are effectively combined together to explore the best discriminating power and improve the classification accuracy. Compared with the holistic matching methods, the local matching method is robust to variations in illumination, expression, and pose, etc. The experimental results show that the proposed method can improve the face recognition accuracy and is robust to variations in illumination and expression.