Rajagopalan A N, Chellappa Rama, Koterba Nathan T
Image Processing and Computer Vision Laboratory, Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai 600 036, India.
IEEE Trans Image Process. 2005 Jun;14(6):832-43. doi: 10.1109/tip.2005.847288.
We propose a new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter. The traditional eigenface recognition (EFR) method, which is based on PCA, works quite well when the input test patterns are faces. However, when confronted with the more general task of recognizing faces appearing against a background, the performance of the EFR method can be quite poor. It may miss faces completely or may wrongly associate many of the background image patterns to faces in the training set. In order to improve performance in the presence of background, we argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigenbackground space is constructed corresponding to the given test image and this space in conjunction with the eigenface space is used to impart robustness. A suitable classifier is derived to distinguish nonface patterns from faces. When tested on images depicting face recognition in real situations against cluttered background, the performance of the proposed method is quite good with fewer false alarms.
我们在主成分分析（PCA）框架内提出了一种新方法，用于在存在杂波的情况下稳健地识别面部。基于PCA的传统特征脸识别（EFR）方法，当输入测试模式为面部时效果很好。然而，当面对识别出现在背景中的面部这一更为一般的任务时，EFR方法的性能可能会相当差。它可能会完全错过面部，或者可能会将许多背景图像模式错误地关联到训练集中的面部。为了在存在背景的情况下提高性能，我们主张学习背景模式的分布，并展示如何针对给定的测试图像做到这一点。对应于给定测试图像构建一个特征背景空间，并且这个空间与特征脸空间一起用于增强鲁棒性。推导了一个合适的分类器以区分非面部模式和面部。当在描绘杂乱背景下实际场景中的面部识别的图像上进行测试时，所提出方法的性能相当好，误报较少。
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