A new class of convolutional neural networks (SICoNNets) and their Application to Face Detection
Faculty of Computing, Health and Science
School of Engineering and Mathematics
Artificial neural networks (ANNs), evolved from biological insights, have equipped computers with the capacity to actually learn from examples using real world data. With this remarkable ability, ANNs are able to extract patterns and detect trends that are too complex to be noticed or perceived by either humans or classical computer techniques. Nevertheless, as the amount of data to be processed increases significantly there is a demand for developing other types of artificial neural networks to perform complex pattern recognition tasks. In this article, a new class of convolutional neural networks, namely shunting inhibitory convolutional neural networks (SICoNNets), is introduced, and a training algorithm is developed using supervised learning based on resilient backpropagation with momentum. Three different network topologies, ranging from fully-connected to partially-connected, are implemented and trained to discriminate between face and non-face patterns. All three architectures achieve more than 96% correct face classification; the best architecture achieves 97.6% correct face classification at a false alarm rate of 3.4%.