EIGENFACES FOR RECOGNITION
EIGENFACES FOR RECOGNITION
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DOI：
10.1162/jocn.1991.3.1.71
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发表时间：
1991-12-01
影响因子：
3.2
通讯作者：
PENTLAND, A
中科院分区：
文献类型：
--
作者：
TURK, M;PENTLAND, A
我们已经开发了一种近实时计算机系统，它能够定位和跟踪对象的头部，然后通过将面部特征与已知个体的面部特征进行比较来识别人物。该系统所采用的计算方法是由生理学、信息论以及近实时性能和准确性的实际要求所推动的。我们的方法将人脸识别问题视为一个本质上的二维（2 - D）识别问题，而不需要恢复三维几何形状，这利用了人脸通常是直立的这一事实，因此可以用一小组二维特征视图来描述。该系统通过将人脸图像投影到一个跨越已知人脸图像之间显著差异的特征空间来运行。这些显著特征被称为“特征脸”，因为它们是人脸集合的特征向量（主成分）；它们不一定对应于眼睛、耳朵和鼻子等特征。投影操作通过特征脸特征的加权和来表征单个人脸，因此要识别某张特定的脸，只需要将这些权重与已知个体的权重进行比较。我们的方法的一些特殊优势在于，它提供了以无监督的方式学习并随后识别新面孔的能力，并且使用神经网络架构很容易实现。
We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characterstic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.