Human face detection system

Abstract

Object detection is a fundamental problem in computer vision. Detection of faces, in particular, is a critical part of face recognition and critical for systems which interact with users visually. We present a view-based approach implemented with artificial neural networks for face detection. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve theaccuracy. In the end we performed sensitivity analysis on the networks, and present empirical results on a large test set.