中文摘要：

提出了一种基于Gabor小波人脸特征和模型自适应算法的新鲁棒人脸识别方法。该方法在真实识别前,通过用与真实识别相同的环境条件下所获得的人脸图像数据对原始模型进行更新补偿,实现了模型自适应。该模型自适应更新算法是加性的,其具有较低的时间和空间复杂度。通过模型自适应更新,新方法可以有效地减少模型和识别数据间的失配,从而提高识别率。在AT＆T和MIT-CBCL人脸数据库上的测试结果表明,该方法是有效的。

英文摘要：

This paper proposed a robust face recognition algorithm based on Gabor wavelet representations and model adaptation. The models used in this work were from linear associative memory method and fast compensated by adaptively learning from the given facial data, which were obtained in same condition as testing. The proposed adaptation algorithm is incremental. It has low time and space complexity. By compensating models, this method can efficiently reduce the mismatch between models and testing data, substantially improving the performance of classifier. The new recognition method was tested using two widely used face datasets：AT＆T and MIT-CBCL face database. Results indicate that the algorithm is effective. And due to the computational simplicity, the algorithm is also efficient.