Neural Hopfield-ensemble for multi-class head pose detection

Multi-class object detection is perhaps the most important task for many computer vision systems and mobile robots. In this work we will show that Hopfield Neural Network (HNN) ensembles can successfully detect and classify objects from several classes by taking advantage of head-pose estimation. The single HNNs are using pixel sums of Haarlike features as input, resulting in HNNs with a small number of neurons. An advantage of using these in ensembles is their compact form. Although it was shown that such HNNs can only memorise few patterns, by utilising a naive-Bayes mechanism we were able to exploit the multi-class ability of single HNNs within an ensemble. In this work we report successful head pose classification, which presents a 4-class problem (3 poses + negatives). Results show that successful classification can be achieved with small training sets and ensembles, making this approach an interesting choice for online learning and robotics.