Multi-Class Ada-Boost Classification of Object Poses through Visual and Infrared Image Information Fusion
Paper in proceedings, 2012

This paper presents a novel method for pose classification using fusion of visual and thermal infrared(IR) images. We propose a novel tree structure multi-class classification scheme with visual and IR sub-classifiers. These sub-classifiers are different from the conventional one-against-all or one-against-one strategies, where we handle the multi-class problem directly. We propose to use an accuracy score for the fusion of visual and IR subclassifiers. In addition, we propose to use the original Haar features plus an extra one, and a multi-threshold weak learner to obtain weak hypothesis. The experimental results on a visual and IR image dataset containing 3018 face images in three poses show that the proposed classifier achieves high classification rate of 99.50% on the test set. Comparisons are made to a fused one-vs-all method, a classifier with visual band only, and a classifier with IR band only. Results provide further support to the proposed method.

Author

Mohamed Hashim Changrampadi

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

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

Proceedings - International Conference on Pattern Recognition, 21st ICPR 2012, Tsukuba,Japan, 11-15 November 2012

1051-4651 (ISSN)

2865-2868

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Computer and Information Science

ISBN

978-499064410-9

More information

Created

10/7/2017