单演滤波与局部量化模式相结合的人脸识别方法  

Face recognition based on combination of monogenic filtering and local quantitative pattern

作　　者：闫海停[1] 王玲[1] 李昆明[1] 刘机福[1] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《计算机应用》2013年第9期2671-2674,共4页journal of Computer Applications

摘　　要：针对传统人脸识别方法提取的特征维数较高和计算量较大的缺点,提出了一种基于单演滤波与局部量化模式(LQP)相结合的人脸特征提取方法。首先,通过对人脸图像进行多尺度的单演滤波获得图像的包括局部幅值、局部方向和局部相位的多模式单演特征;然后,用LQP算子对图像中的每个像素点的三种单演特征进行编码,得到每个尺度滤波器下的LQP模式图;最后,将这些LQP模式图分块、统计每一块的直方图并级联作为人脸识别特征。在ORL和CAS-PEAL人脸库上对所提算法进行的测试结果表明,该算法能够以较低维数的特征取得较高的识别率,可以有效降低算法的计算复杂度。Concerning the disadvantages of traditional face recognition methods, such as high dimension of extracted feature, higher computational complexity, a new method of face recognition combining monogenic filtering with Local Quantiztative Pattern （LQP） was proposed. Firstly, the muhi-modal monogenic features of local amplitude, local orientation and local phase were extracted by a multi-scale monogenic filter; secondly, the LQP operator was used to get LQP feature maps by encoding the three kinds of monogenic features in each pixel; finally, the LQP feature maps were divided into several blocks, from which spatial histograms were extracted and connected as the face feature. 0RL and CAS-PEAL face databases were used to test the proposed algorithm and the recognition rates were higher than all the other methods used in the experiments. The resuhs validate that the proposed method has higher recognition accuracy and can reduce the computational complexity significantly.

关 键 词：人脸识别 单演滤波 局部量化模式 K均值聚类 码本 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]