We propose a (near) real-time, face detector using a. cascade of parallel 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 such that the complexity of base networks increases. Our proposed approach achieves up to 94% detection rate. (CMU+MIT test set) and 3-4% frames/sec, detection speed on a normal PC (P-IV. 3.0GHz).
展开▼
