Face Tracking Using Adaptive Appearance Models and Convolutional Neural Network

Boguslaw Rymut and Bogdan Kwolek: Face Tracking Using Adaptive Appearance Models and Convolutional Neural Network; Int. Conf. on Hybrid Artificial Intelligence Systems, 2011, Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, Vol. 6678, Springer Berlin Heidelberg, 2011, pp. 271-279.

Abstract

One inherent problem of online learning based trackers is drift consisting in a gradual accommodation of the tracker to non-targets. This paper proposes an algorithm that does not suffer from the template drift inherent in a naive implementation of the online appearance models. The tracking is done via particle swarm optimization algorithm built on adaptive appearance models. The convolutional neural network based face detections are employed to support the re-diversification of the swarm in the course of the tracking. Such candidate solutions vote simultaneously towards true location of the face through correcting the fitness function. In particular, the hybrid algorithm has better recovery capability in case of tracking failure.