Fast Face Detection Using a Cascade of Neural Network Ensembles

Abstract

We propose a (near) real-time face detector using a cascade of neural network (NN) ensembles for enhanced detection accuracy and efficiency. First, we form a coordinated NN ensemble by sequentially training a set of neural networks with the same topology. The training implicitly partitions the face space into a number of disjoint regions, and each NN is specialized in a specific sub-region. Second, to reduce the total computation cost for the face detection, a series of NN ensembles are cascaded by increasing complexity of base networks. Simpler NN ensembles are used at earlier stages in the cascade, which are able to reject a majority of non-face patterns in the backgrounds. Our proposed approach achieves up to 94% detection rate on the CMU+MIT test set, a 98% detection rate on a set of video sequences and 3-4 frames/sec. detection speed on a normal PC (P-IV, 3.0GHz).