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用于面部验证的Tom-vs-Pete分类器和身份保持对齐

Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification
课程网址: http://videolectures.net/bmvc2012_berg_face_verification/  
主讲教师: Thomas Berg
开课单位: 哥伦比亚大学
开课时间: 2012-10-09
课程语种: 英语
中文简介:
提出了一种利用参考面集的人脸验证方法,该方法通过识别面与测试面分离,用标识和人脸部件位置进行标识。引用集有两种使用方式。首先,我们使用它来执行身份保护。对齐,以一种减少姿态和表情差异的方式扭曲面部,但保留表明身份的差异。其次,使用对齐的面孔,我们学习了一组大的身份分类器,每个分类器只训练了两个人的图像。我们把这些叫做“汤姆vs彼得”分类器强调它们的二进制性质。我们汇集了这些分类器的集合,这些分类器能够在各种各样的主题之间进行区分,并使用它们的输出作为相同或不同的面部对上分类器的特征。我们在野生基准测试中对我们的方法进行了评估,获得了93.10%的准确率,显著提高了已发表的技术水平。
课程简介: We propose a method of face verification that takes advantage of a reference set of faces, disjoint by identity from the test faces, labeled with identity and face part locations. The reference set is used in two ways. First, we use it to perform an “identity-preserving” alignment, warping the faces in a way that reduces differences due to pose and expression but preserves differences that indicate identity. Second, using the aligned faces, we learn a large set of identity classifiers, each trained on images of just two people. We call these “Tom-vs-Pete” classifiers to stress their binary nature. We assemble a collection of these classifiers able to discriminate among a wide variety of subjects and use their outputs as features in a same-or-different classifier on face pairs. We evaluate our method on the Labeled Faces in the Wild benchmark, achieving an accuracy of 93.10%, significantly improving on the published state of the art.
关 键 词: 面部验证方法; 身份脱节; 面部位置标记
课程来源: 视频讲座网
最后编审: 2021-01-31:nkq
阅读次数: 43