Naive Bayes face-nonface classifier: a study of preprocessing and feature extraction techniques

This paper presents a classifier of face and nonface patterns that is based on the naive Bayes model. Using this classifier as a tool. We analyze the effects on classification performance of preprocessing, feature extraction and classifier combination techniques. Our analysis shows that image normalization techniques that reduce the effects of different lighting conditions improve face-nonface classification significantly. In addition, techniques such as background masking and combining classifiers that use different feature vectors are shown to enhance classification performance. Over a test set of 12,000 patterns, the combined classifier using four feature vectors has correct detection rates (CDRs) of 96.2% and 99.2% at false detection rates (FDRs) of 1% and 5%, respectively.