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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Subject-Specific and Pose-Oriented Facial Features for Face Recognition Across Poses
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Subject-Specific and Pose-Oriented Facial Features for Face Recognition Across Poses

机译：跨姿势的面部识别的特定于对象和面向姿势的面部特征

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摘要

Most face recognition scenarios assume that frontal faces or mug shots are available for enrollment to the database, faces of other poses are collected in the probe set. Given a face from the probe set, one needs to determine whether a match in the database exists. This is under the assumption that in forensic applications, most suspects have their mug shots available in the database, and face recognition aims at recognizing the suspects when their faces of various poses are captured by a surveillance camera. This paper considers a different scenario: given a face with multiple poses available, which may or may not include a mug shot, develop a method to recognize the face with poses different from those captured. That is, given two disjoint sets of poses of a face, one for enrollment and the other for recognition, this paper reports a method best for handling such cases. The proposed method includes feature extraction and classification. For feature extraction, we first cluster the poses of each subject's face in the enrollment set into a few pose classes and then decompose the appearance of the face in each pose class using Embedded Hidden Markov Model, which allows us to define a set of subject-specific and pose-priented (SSPO) facial components for each subject. For classification, an Adaboost weighting scheme is used to fuse the component classifiers with SSPO component features. The proposed method is proven to outperform other approaches, including a component-based classifier with local facial features cropped manually, in an extensive performance evaluation study.
机译：大多数人脸识别方案都假设可以将正面人脸或面部照片注册到数据库中，而其他姿势的人脸则收集在探针集中。给定探针组的面孔，就需要确定数据库中是否存在匹配项。这是基于以下假设：在法医应用中，大多数犯罪嫌疑人的面部照片都可以在数据库中获得，并且面部识别旨在在监视摄像机捕获其各种姿势的面孔时识别犯罪嫌疑人。本文考虑了另一种情况：给定具有多个姿势的脸部（可能包括或不包括面部照片），开发一种识别脸部姿势与所捕获姿势不同的方法。也就是说，给定两张不相交的脸部姿势，一组用于注册，另一组用于识别，本文报告了一种最适合处理此类情况的方法。所提出的方法包括特征提取和分类。为了进行特征提取，我们首先将注册集中每个受试者脸部的姿势聚类为几个姿势类，然后使用“嵌入式隐马尔可夫模型”（Embedded Hidden Markov Model）分解每个姿势类中的脸部外观，从而可以定义一组受试者每个对象的特定和姿势优先（SSPO）面部组件。对于分类，使用Adaboost加权方案将组件分类器与SSPO组件特征融合在一起。在广泛的性能评估研究中，该方法被证明优于其他方法，包括基于人工分类的具有局部面部特征的基于组件的分类器。

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