摘要
针对现有的局部方向模式LDP(Local Directional Pattern)方法仅利用了图像自身的LDP特征的问题,提出将LDP特征直方图与贝叶斯(Bayes)模型相结合的方法,从而有效使用人脸图像的先验信息,以提高人脸的识别率。第一步,在相互独立的训练集上,学习同类样本图像和异类样本图像的LDP直方图特征相似度的先验信息,并估计类条件概率密度函数(同类样本与异类样本分别进行计算);第二步,利用人脸图像的LDP直方图来比较该图像是否为某一类型图像的概率数值大小;第三步,使用贝叶斯规则进行分类。仿真结果证明,在ORL库与Yale库上,与传统PCA,LBP和LDP算法相比,所提方法得到的人脸识别率均有显著提升。
For the existing Local Directional Pattern method,the LDP feature of the image itself is only proposed,and a method of combining the LDP feature histogram and the Bayesian model is proposed,which effectively improves the recognition rate by using the face image test information.The method firstly learned the prior information of similarity of LDP histogram in the same class and in different class,and evaluated the same class conditional probability density function and different class conditional probability density function.When a probe image was discriminated,the method calculated the similarity of the probe and a template image in database using their LDP histogram features,and then evalua-ted the post erior probability of the pair images coming from the same person.Finally,the probe image was classified by Bayes rule.The propose method fuses the LDP feature and prior information into face data.Experiment results on ORL and Yale databases show that it is valid and the recognition accuracy rates are improved considerably on ORL database and Yale database compared with PCA method,LBP method and LDP method.
作者
王燕
李鑫
WANG Yan;LI Xin(College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China)
出处
《计算机科学》
CSCD
北大核心
2017年第12期283-286,291,共5页
Computer Science
基金
国家自然科学基金:多民族欠发达地区传染病传播动力学特征分析与建模(61263019)资助
