自适应阈值及加权局部二值模式的人脸识别

摘要

针对局部二值模式(LBP)和中心对称局部二值模式(CS-LBP)方法描述图像纹理特征时，阈值不能自动选取并且图像中不同子块的贡献也没有进行区分的问题，该文提出一种自适应阈值及加权的局部二值模式方法。首先，将图像进行分块，采用设定的自适应阈值提取每个子块的LBP或CS-LBP纹理直方图；然后，将各子图像的信息熵作为直方图的加权依据，对每个子块对应的直方图进行自适应加权，并将所有子块的直方图连接成最终的纹理特征；最后，通过快速计算图像均值加快了算法的计算速度。在人脸数据库上进行的实验证明，利用该文提出的方法提取纹理特征，并结合最近邻分类法可以得到较高的正确识别率。%A new method called weighted Local Binary Pattern (LBP) with adaptive threshold is proposed in this paper to address the shortcomings of LBP and Center Symmetric Local Binary Pattern (CS-LBP), using unflexible threshold and non-discriminating respective sub-patches based on different textures. Firstly, the image is divided into several sub-images and LBP or CS-LBP texture histograms are extracted respectively from each sub-image based on the adaptive threshold. Then, the proposed algorithm adaptively weighted the LBP or CS-LBP histograms of sub-patches with information entropy as their basis and connected all histograms serially to create a final texture descriptor. Finally, the improved efficiency of the proposed algorithm is achieved by speeding up the computation of the average of an image. The experimental results by face databases show that a higher recognition accuracy can be obtained by employing the proposed method with nearest neighbor classification.