检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
机构地区:[1]湖南大学信息科学与工程学院,长沙410082
出 处:《光电工程》2016年第3期80-87,共8页Opto-Electronic Engineering
基 金:国家自然科学基金资助项目(60972114)
摘 要:针对人脸全局特征用于人脸验证存在的局限性,本文在Joint Bayesian人脸识别方法的基础上提出了基于局部贝叶斯分类器融合的人脸验证方法。该方法使用约束局部模型(CLM)在人脸上标注27个局部特征点,提取以这些特征点为中心的人脸块,并将它们进一步划分为互不重叠的若干个单元格;将这些人脸块的局部二值模式(LBP)特征通过Joint Bayesian统计训练得到多个局部分类器;最后利用逻辑回归模型将局部分类器融合为人脸验证分类器。在LFW(Labeled Face in the Wild)和WDRef(Wide and Deep Reference)数据库上进行了性能验证实验,实验结果表明该方法的性能要优于Joint Bayesian和其他现有典型分类器。A novel face verification model based on confusing local Bayesian classifier will be proposed to eliminate the limitation of using global face feature for face verification. Firstly, 27 landmarks were located based on a Constrain Local Model(CLM) model. Then, face patches centered on each landmark were extracted and further split into non-overlapping cells. These face patches' Local Binary Pattern(LBP) feature can be used for creating local Bayesian classifiers by doing Joint Bayesian training. And the local classifiers were integrated in the framework of logistic regression. Finally, a face verification model was taken shape. The original approach was evaluated on the Labeled Face in the Wild(LFW) and Wide and Deep Reference(WDRef) databases. The experimental results show that our method is superior to Joint Bayesian method and most of the state-of-the-art classifiers.
关 键 词:人脸验证 LBP 贝叶斯分类器 分类器融合 逻辑回归
分 类 号:TN219[电子电信—物理电子学] TP391[自动化与计算机技术—计算机应用技术]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.229