Local Normalization with Optimal Adaptive Correlation for Automatic and Robust Face Detection on Video Sequences

This paper proposes an automatic and robust method to detect human faces from video sequences that combines feature extraction and face detection based on local normalization, Gabor wavelets transform and Adaboost algorithm. The key step and the main contribution of this work is the incorporation of a normalization technique based on local histograms with optimal adaptive correlation (OAC) technique to alleviate a common problem in conventional face detection methods: inconsistent performance due to the sensitivity to illumination variations such as local shadowing, noise and occlusion. This approach uses a cascade of classifiers to adopt a coarse-to-fine strategy to achieve higher detection rate with lower false positives. The experimental results demonstrate a significant performance improvement by local normalization over method without normalizations in real video sequences with a wide range of facial variations in color, position, scale, and varying lighting conditions.