Face tracking using adaptive appearance models and convolutional neural network

Authors:
Boguslaw Rymut;Bogdan Kwolek
Affiliations:
Rzeszów University of Technology, Rzeszów, Poland;Rzeszów University of Technology, Rzeszów, Poland
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Year:
2011

Citing 11
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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.