摘要
传统Retinex算法在侧光严重的情况下难以消除阴影,为此提出一个对数形式的传导函数,取得了很好的光照补偿效果。为提高人脸识别率,将该问题看成一个典型的模式分类问题,提出基于局部二值模式(LBP)特征的支持向量机(SVM)人脸识别方法,使用"一对一"的方法将多类问题转化为SVM分类器可以解决的两类问题,实现了高效的人脸识别。在CMU PIE、AR、CAS-PEAL以及自行采集的人脸库上进行了仿真实验,结果表明该方法能够有效地去除光照影响,相对传统方法具有较优的识别性能。
With serious sidelight, it is difficult for the traditional algorithm to eliminate shadows. To improve the illumination compensation effect, a logarithmic transformation function was presented. In order to improve the performance of face recognition, by taking this problem as a classic pattern classification problem, a new method combining Local Binary Pattern （LBP） and Support Vector Machine （SVM） was proposed. One-against-one was used to convert multi-class problem to two-class problem, that can be used by SVM. Simulation experiments were conducted on the database of CMU PIE, AR, CAS-PEAL and one face database collected by the authors. The results show that lighting effects can be well eliminated and the proposed method performs better than the traditional ones.
出处
《计算机应用》
CSCD
北大核心
2013年第2期507-510,514,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61202191)
中央高校基本科研业务费专项资金资助项目(SWJTU12CX095)
关键词
人脸识别
光照
局部二值模式
支持向量机
视网膜皮层
face recognition
lighting
Local Binary Pattern （LBP）
Support Vector Machine （SVM）
Retinex
