【摘要】 当前的人脸识别算法在理想环境下的识别正确率高,自适应能力强;但是在非理想环境下,人脸识别正确率急剧下降。为了提高人脸识别结果的稳定性,设计了稀疏表示和支持向量机相融合的非理想环境人脸识别算法。首先,提取非理想环境人脸的特征,并构建非理想环境人脸识别的特征字典;然后,采用特征字典对非理想环境人脸识别训练样本和测试样本进行处理,构建非理想环境人脸识别的学习样本;最后,采用支持向量机建立非理想环境人脸识别的分类器来对非理想环境人脸进行识别,并采用多个标准人脸数据库对所提非理想环境人脸识别算法进行测试。文中算法的非理想环境人脸识别正确率高,误识率和拒识率低,相对于其他人脸识别算法,其更适应环境的变化,对非理想环境人脸识别的整体效果更优,而且提高了非理想环境人脸识别的效率,具有十分明显的优越性。更多还原

【Abstract】 Currently face recognition algorithms have high recognition accuracy and strong adaptive ability in ideal environment,but in non-ideal environment,the accuracy of face recognition declines sharply.In order to improve the stability of face recognition results,a non-ideal environment face recognition algorithm based on sparse representation and support vector machine fusion is designed.Firstly,the feature dictionary of face recognition in non-ideal environment is constructed,then the training samples and test samples of face recognition in non-ideal environment are processed by feature dictionary,and the learning samples of face re-cognition in non-ideal environment are constructed.Finally,the classifier of face recognition in non-ideal environment is established by using support vector machine,and face recognition in non-ideal environment is processed.A number of standard face databases are used to test the non-ideal environment face recognition algorithm.The non-ideal environment face recognition accuracy of this algorithm is high,the false recognition rate and rejection rate of non-ideal environment face recognition are low.Compared with other face recognition algorithms,it is more adaptable to environmental changes,and the overall recognition effect of non-ideal environment face is better.It improves the efficiency of face recognition in non-ideal environment and has obvious advantages.更多还原