【摘要】 人脸识别是一种常用的生物特征识别技术,广泛应用于门禁考勤、公安司法等领域。光照、人脸表情与姿态、遮挡等采集条件的变化对现有人脸识别方法影响较大,限制了其应用。提出了一种基于曲波变换和余弦测度的人脸识别方法,以提高人脸识别对采集条件的鲁棒性。首先,对待识别人脸图像进行曲波变换,依据曲波系数检测人脸区域的关键点;然后,提取各关键点在不同尺度和方向上的曲波特征,构建人脸特征描述子;最后,依据余弦测度、累加和运算和极值运算求取人脸的最优匹配结果。仿真实验表明,所提方法对光照、姿态、表情和遮挡等变化的鲁棒性强,且识别性能好。更多还原

【Abstract】 Face recognition is common biometric identification,widely used in access control,public security and justice,and so on.Existing face recognition methods are greatly influenced by the change of acquisition conditions on illumination,facial expression,pose and occlusion,limiting the application of face recognition technology.This paper proposed a face recognition method based on curvelet transform and cosine rules,to improve the robustness on acquisition conditions.First,we executed curvelet transform on face image,and detected key points according to curvelet coefficients.Then,we extracted curvelet features from key points with different scales and directions,and obtained a face descriptor.Finally,the optimal matching result was computed through cosine rules,cumulative and extreme operation.Experiments show that the proposed method is robust to changes of illumination,facial expression,pose and occlusion,and has good recognition performance.更多还原