ICRA 2011 Paper Abstract

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Paper WeP114.3

Yan, Haibin (National University of Singapore), Ang Jr, Marcelo H (National University of Singapore), Poo, Jim A.N. (National University of Singapore)

Weighted Biased Linear Discriminant Analysis for Misalignment-Robust Facial Expression Recognition

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation III" (WeP114), Wednesday, May 11, 2011, 14:10−14:25, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords Computer Vision for Robotics and Automation, Recognition

Abstract

We investigate in this paper the problem of misalignment-robust facial expression recognition. To the best of our knowledge, this problem has not been formally addressed in the literature. Most existing facial expression recognition methods, however, can only work well when face images are well-aligned. In many real world applications such as human robot interaction and visual surveillance, it is still very challenging to obtain well-aligned face images for expression recognition due to currently imperfect vision techniques, especially under uncontrolled conditions. Motivated by the fact that interclass facial images with small differences are more easily mis-classified than those with large differences, we propose a biased linear discriminant analysis (BLDA) method by imposing large penalties on interclass samples with small differences and small penalties on those samples with large differences simultaneously, such that more discriminative features can be extracted for recognition. Moreover, we generate more virtually misaligned facial expression samples and assign different weights to them according to their occurrence probabilities in the testing phase to learn a weighted BLDA (WBLDA) feature space to extract misalignment-robust discriminative features for recognition. Experimental results on two widely used face databases are presented to show the efficacy of the proposed method.

 

 

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