Vision and Modeling Group, The Media Laboratory Massachusetts, Institute of Technology.
J Cogn Neurosci. 1991 Winter;3(1):71-86. doi: 10.1162/jocn.1991.3.1.71.
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 characteristic 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.
我们开发了一个接近实时的计算机系统，可以定位和跟踪对象的头部，然后通过比较面部特征与已知个体的特征来识别该对象。该系统采用的计算方法既受生理学和信息理论的启发，也受接近实时性能和准确性的实际要求的推动。我们的方法将人脸识别问题视为本质上的二维（2-D）识别问题，而不需要恢复三维几何形状，利用了这样一个事实，即人脸通常是直立的，因此可以用一小组 2-D 特征视图来描述。该系统通过将人脸图像投影到跨越已知人脸图像的显著变化的特征空间上来工作。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描述一个人的面部特征，因此识别特定的面部只需要将这些权重与已知个体的权重进行比较。我们的方法有一些特别的优势，它提供了以无监督的方式学习和后来识别新面孔的能力，并且很容易使用神经网络架构来实现。
