Face Recognition with Improved Deep Belief Networks

Deep learning techniques have become the state-of-theart approach for classification in artificial intelligence, and applied in many widespread subjects. Deep Belief Networks ( DBNs) are one of the most successful models. DBNs consist of many layers of hidden factors along with a greedy layer-wise unsupervised learning algorithm. In our paper, we brought forward an approach to face recognition based on dropout DBNs, which made good performances on small training sets.