Title :
Spontaneous facial expression recognition by using feature-level fusion of visible and thermal infrared images
Author :
Zhaoyu Wang ; Wang, Zhaoyu
Author_Institution :
Key Lab. of Comput. & Communicating Software of Anhui Province, Univ. of Sci. & Technol. of China, Hefei, China
Abstract :
In this paper, we propose a spontaneous facial expression recognition method by using feature-level fusion of visible and thermal infrared facial images. Firstly, the appearance features of visible images and statistic parameters of thermal infrared difference images are extracted. Then, analysis of variance is adopted to select the optimal feature subsets from both visible and thermal ones. These selected features are combined as the input of a K-Nearest Neighbors classifier. We experimentally evaluate the effectiveness of the proposed method on USTC-NVIE database. The experimental results show that fusion of visible and thermal infrared features can improve the accuracy rate of negative expressions and reduce the discrepancy. Thus, it can improve the expression recognition performance.
Keywords :
face recognition; feature extraction; image classification; image fusion; infrared imaging; K-nearest neighbors classifier; USTC-NVIE database; feature-level fusion; spontaneous facial expression recognition; thermal infrared difference image extraction; thermal infrared images; visible infrared images; Accuracy; Active appearance model; Face recognition; Feature extraction; Image recognition; Shape; Vectors; Spontaneous facial expression recognition; feature-level fusion; thermal infrared image; visible image;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on
Conference_Location :
Santander
Print_ISBN :
978-1-4577-1621-8
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2011.6064564