Abstract: Face recognition for one training sample is one of the most challenge tasks. Many developed systems, that work very well when there are sufficient representative training samples, often become less effective or low accuracy for one training sample. After analysing the local facial features, several significant features to represent the human face were selected. Then a novel algorithm of feature extraction for single sample by exploiting repeatedly the selected face features was proposed following the Boosting methods. Finally, two face recognition systems were built by combining whole face features with local face features—— voting-based method with multi-features and mothod with repeatedly used features. The experiments show that the developed systems have good performance in comparison with the existing system.

Key words: Boosting methods, repeatedly used features, face recognition

摘要： 单幅图像的人脸识别问题目前研究较少,而许多识别算法一旦应用到单幅训练图像的人脸库时,识别率会急剧下降。通过研究人脸的各个局部特征对识别人脸的影响,筛选出几个最能表达人脸信息的局部特征,然后利用Boosting思想,为从单个图像样本中挖掘更多的信息,重复使用人脸特征,将人脸的整体特征和局部特征结合起来构造了两个人脸识别系统———多特征投票法和复用特征法。

关键词: Boosting思想, 复用特征, 人脸识别