【摘要】 为改善多姿态人脸识别效果,设计一种稀疏编码和机器学习相融合的多姿态人脸识别算法.首先对多姿态人脸进行采集和预处理,并提取基于稀疏编码的人脸图像特征;然后采用主成分分析对特征进行处理,降低多姿态人脸识别的特征维数,提高多姿态人脸识别效率;最后采用机器学习算法中的支持向量机建立多姿态人脸识别分类器,并采用标准人脸数据库和多姿态人脸数据库对算法性能进行验证.验证结果表明,该算法可有效提高多姿态人脸识别正确率,大幅度减少多姿态人脸的平均识别时间,取得了比对比算法更优的识别结果,从而验证了该算法的优越性.更多还原

【Abstract】 In order to improve the recognition effect of multi-pose face,we designed a multi-pose face recognition algorithm,combining sparse coding and machine learning.Firstly,the multi-pose face was collected and preprocessed,and feature of face image based on sparse coding was extracted.Secondly,the feature was processed by principal component analysis to reduce the feature dimension of multipose face recognition and improve the efficiency of multi-pose face recognition.Finally,the support vector machine of machine learning algorithm was used to establish the classifier of multi-pose face recognition,and the performance of the algorithm was verified by the standard face database and multi-pose face database.The verification results show that the algorithm can effectively improve the accuracy of multi-pose face recognition,greatly reduce the average recognition time of the multi-pose face,and achieve better recognition results than the contrast algorithm,thus the superiority of the algorithm is verified.更多还原