Multi-view Face Pose Classification by Boosting with Weak Hypothesis Fusion Using Visual and Infrared Images
Paper in proceedings, 2012

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.

weak hypothesis fusion

sub-ensemble learning

sequential learning

multi-class AdaBoost

visual and infrared images

Author

Yixiao Yun

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

1949-1952 6288287

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ICASSP.2012.6288287

ISBN

978-146730046-9

More information

Created

10/7/2017