Head Pose Classification by Multi-Class AdaBoost with Fusion of RGB and Depth Images
Paper in proceedings, 2014

This paper addresses issues in multi-class visual object classification, where sequential learning and sensor fusion are exploited in a unified framework. We adopt a novel method for head pose classification using RGB and depth images. The main contribution of this paper is a multi-class AdaBoost classification framework where information obtained from RGB and depth modalities interactively complement each other. This is achieved by learning weak hypotheses for RGB and depth modalities independently with the same sampling weight in the boosting structure, and then fusing them through learning a sub-ensemble. Experiments are conducted on a Kinect RGB-D face image dataset containing 4098 face images in 5 different poses. Results have shown good performance in obtaining high classification rate (99.76%) with low false alarms on the dataset.

RGB and depth images

AdaBoost

visual information fusion

head pose classification

Author

Yixiao Yun

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

Mohamed Hashim Changrampadi

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

Irene Yu-Hua Gu

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

1st International Conference on Signal Processing and Integrated Networks (SPIN)

174-177

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Information Science

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

ISBN

978-1-4799-2866-8

More information

Created

10/8/2017