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Xie, S., Shan, S., Chen, X., Meng, X. and Gao, W. (2009) Learned local ga-borpatterns for face representation and recogni- tion. Signal Processing, 89, pp. 2333-2344.

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  • 标题: 基于鲁棒的局部二值模式人脸识别算法A Novel Face Recognition Algorithm Based on Robust Local Binary Pattern

    作者: 程雷鸣, 其木苏荣, 靳薇

    关键字: 人脸识别, 鲁棒的局部二值模式, Robust函数, 马氏距离Face Recognition; Robust Local Binary Pattern; Robust Function; Mahalanobis Distance

    期刊名称: 《Computer Science and Application》, Vol.3 No.8, 2013-11-28

    摘要: 本文针对LBP算法特征包含outlier和维度过高的问题提出了一种基于鲁棒的局部二值模式(RobustLBP)的快速有效的人脸识别算法。RobustLBP算法的思想是在LBP算法的基础上加上一个Robust函数除去outlier达到降维的目的。首先通过计算LBP特征各个维度和中心元素的马氏距离作为Robust函数的输入,使得Robust函数收敛估算出重要信息。然后利用这些信息求出变换矩阵除去原始LBP特征的outlier。最后比对降维后特征间的卡方距离实现人脸识别。在FERET、CAS-PEAL-R1、LFW人脸数据库上的实验证明本文提出方法在是人脸识别上具有优越性。This paper is aimed at solving the problems that LBP feature contains outlier and the dimension of LBP fea- ture is too high, and a fast and effective face recognition algorithm based on Robust Local Binary Pattern is proposed. The main idea of RobustLBP is setting a Robust function on the basis of original LBP. First, it calculates the Maha- lanobis distance between the mean vector and every dimension as the argument of Robust function and estimates a set of important information by making Robust function convergence. Then, it obtains a transformation matrix which is used to reject outlier of original feature by using the information. Lastly, it compares the Chi-square distance among the features after reducing dimension in order to complete face recognition. Extensive experiments on FERET, CAS- PEAL-R1 and LFW face databases validate the effectiveness of face recognition.