Evolutionary Optimization of Neural Networks for Face Detection

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a neural network, e.g., in the Viisage-FaceFINDER Ⓡ video surveillance system. We describe the optimization of such a network by a hybrid algorithm combining evolutionary computation and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy

Evolutionary multi-objective optimization of neural networks for face detection

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g., in the Viisage-FaceFINDER video surveillance system. We describe the optimization of such a NN by a hybrid algorithm combining evolutionary multi-objective optimization (EMO) and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy. We compare an EMO and a single objective approach, both with online search strategy adaptation. It turns out that EMO is preferable to the single objective approach in several respects