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基于水平分量优先原则的RDW-LBP人脸识别算法 被引量：4

Face recognition algorithm using RDW-LBP based on horizontal component prior principle
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摘要 首先分析了人脸图像不同的方向性细节对识别率的影响,提出了水平分量优先原则。结合该原则提出了一种基于多尺度区域性-方向性加权的规范型二元局部纹理描述算子RDW-LBP的鲁棒人脸识别算法。算法通过多尺度Haar小波分解,提取多级尺度分量和含有最多有效识别细节的一级水平细节分量,组成待分析系数子图矩阵M。计算矩阵M的RDW-LBP纹理特征图谱,结合子区域剖分,连接子区域特征共同组成人脸图像特征向量,最后使用基于Chi-Square距离的分类器进行识别。 In order to improve the recognition rate and robustness of face recognition algorithms, the impact of different directional details of facial images on recognition rate is analyzed, and a Horizontal component prior principle （HCPP） is proposed. Then a robust face recognition algorithm based on Regional directional weighted local binary pattern （RDW-LBP） is proposed, in which HCPP is employed. The face image is decomposed by multi-scale Haar wavelet to extract multi-level scale coefficients and the first level horizontal detail coefficients, which contain the most effective details are used for recognition. These coefficients are regarded as the elements of matrix M to be analyzed. Then matrix M is divided into several regions, from which the RDW-LBP feature distributions are extracted and concatenated into an enhanced feature vector for use as a facial descriptor. Finally, the classifier based on Chi-Square distance is used for recognition. The correctness and validity of HCPP are verified by test results. Compared with LBP descriptor proposed by Ojala, RDW-LBP descriptor improves the recognition rate efficiently without increasing computational complexity.
作者 王莹 李文辉 傅博 李慧盈 倪洪印
机构地区 吉林大学计算机科学与技术学院
出处 《吉林大学学报（工学版）》 EI CAS CSCD 北大核心 2011年第3期750-757,共8页 Journal of Jilin University:Engineering and Technology Edition
基金 '863'国家高技术研究发展计划项目(2008AA10Z224) 国家自然科学基金项目(60873147 41001302)
关键词 计算机应用 人脸识别 区域性-方向性加权的二元局部纹理 多尺度小波分解 直方图特征 computer application face recognition RDW-LBP multi-scale wavelet decomposition histogram features
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献19

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共引文献126

同被引文献44

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引证文献4

二级引证文献20

吉林大学学报（工学版）
2011年 第3期

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