Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
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We propose a new method within the framework of principal component analysis to robustly recognize faces in the presence of clutter. The traditional eigenface recognition method performs poorly when confronted with the more general task of recognizing faces appearing against a background. It misses faces completely or throws up many false alarms. We argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigen-background space is constructed and this space in conjunction with the eigenface space is used to impart robustness in the presence of background. A suitable classifier is derived to distinguish non-face patterns from faces. When tested on real images, the performance of the proposed method is found to be quite good.