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Multi-view Face Pose Classification by Boosting with Weak Hypothesis Fusion Using Visual and Infrared Images

Yixiao Yun (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012. Kyoto, 25 - 30 March 2012 (1520-6149). p. 1949-1952 . (2012)
[Konferensbidrag, refereegranskat]

This paper proposes a novel method for multi-view face pose classification through sequential learning and sensor fusion. The basic idea is to use face images observed in visual and thermal infrared (IR) bands, with the same sampling weight in a multi-class boosting structure. The main contribution of this paper is a multi-class AdaBoost classification framework where information obtained from visual and infrared bands interactively complement each other. This is achieved by learning weak hypothesis for visual and IR band independently and then fusing the optimized hypothesis sub-ensembles. In addition, an effective feature descriptor is introduced to thermal IR images. Experiments are conducted on a visual and thermal IR image dataset containing 4844 face images in 5 different poses. Results have shown significant increase in classification rate as compared with an existing multi-class AdaBoost algorithm SAMME trained on visual or infrared images alone, as well as a simple baseline classification-fusion algorithm.

Nyckelord: multi-class AdaBoost, weak hypothesis fusion, sub-ensemble learning, visual and infrared images, sequential learning



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Denna post skapades 2012-02-09. Senast ändrad 2013-05-07.
CPL Pubid: 154946

 

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