Rotation Invariant Real-time Face Detection and Recognition System
Rotation Invariant Real-time Face Detection and Recognition System
复制标题
旋转不变实时人脸检测与识别系统
DOI：
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复制
发表时间：
2001
期刊：
影响因子：
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通讯作者：
Purdy Ho
中科院分区：
文献类型：
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作者：
Purdy Ho
在本报告中，开发了一种能够检测和识别正面和旋转面部的面部识别系统。提出并评估了两种关注姿势不变性的人脸识别方法 |整体方法和基于组件的方法。该项目的主要挑战是开发一个能够实时识别不同视角下人脸的系统。这样一个系统的开发将增强当前人脸识别技术的能力和鲁棒性。全脸方法通过对由整个人脸图像的灰度值组成的单个特征向量进行分类来识别人脸。基于组件的方法 (cid:12) 首先定位面部组件并提取它们。这些分量被归一化并组合成单个特征向量以进行分类（cid：12）。支持向量机 (SVM) 用作这两种方法的分类器(cid:12)。针对姿势变化的稳健性在数据库上进行了广泛的测试，该数据库包括深度旋转达约 40° 的面部。在所有测试中，基于组件的方法明显优于全脸方法。尽管这种方法被证明更可靠，但对于实时应用程序来说仍然太慢。这就是为什么采用全脸方法的实时人脸识别系统来识别彩色视频序列中的人的原因。 1 本报告介绍了麻省理工学院脑与认知科学系生物和计算学习中心以及人工智能实验室进行的研究。
In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated | the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under di(cid:11)erent viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach (cid:12)rst locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classi(cid:12)cation. The Support Vector Machine (SVM) is used as the classi(cid:12)er for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 Æ in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach is proven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences. 1 This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and in the Arti(cid:12)cial Intelligence Laboratory at the Massachusetts Institute of Technology.
基于特征脸的人脸建模与识别
DOI：
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发表时间：
2003
期刊：
IPSJ SIG Technical Reports Vol. CVIM-139
影响因子：
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作者：
T.;Shakunaga;F.;Sakaue;Y.;Matsubara
通讯作者：
Matsubara