Fast Face Detection Using Subspace Discriminant Wavelet Features

Abstract

Computation complexity is an important issue for current face detection systems. This paper proposes a subspace approach to capture local discriminative features in the space-frequency domain for fast face detection. Based on orthonormal wavelet packet analysis, we develop a discriminant subspace algorithm to search for the "minimum cost" subspace of the high-dimensional signal space, which leads to a set of wavelet features with maximum class discrimination and dimensionality reduction. Detailed (high frequency) information within local facial areas shows noticeable discrimination ability for face detection problem. We demonstrate the algorithm in the context of detecting frontal view faces in a complex background. Discrete pattern distribution functions and fast likelihood ratio detection are adopted by the system. Because of the reduced dimensionality, feature discrimination and the discrete stochastic model, our face detection system consumes much less computation while the performance is comparable with other reported leading systems.