Facial expression recognition based on AAM-SIFT and adaptive regional weighting

The active appearance model (AAM), one of the most effective facial feature localization methods, is widely used in frontal facial expression recognition. However, non‐frontal facial expression recognition is important in many scenarios. Thus, we propose a new method for facial expression recognitio...

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Bibliographic Details
Published in:IEEJ transactions on electrical and electronic engineering Vol. 10; no. 6; pp. 713 - 722
Main Authors: Ren, Fuji, Huang, Zhong
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.11.2015
Wiley Subscription Services, Inc
Subjects:
Algorithms
appearance model
Face recognition
Facial
Fuzzy
fuzzy c-means clustering
gradient direction histogram
non-frontal facial image
Regional
scale-invariant feature transform
Support vector machines
Weighting
ISSN:1931-4973, 1931-4981
Online Access:Get full text
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Summary:The active appearance model (AAM), one of the most effective facial feature localization methods, is widely used in frontal facial expression recognition. However, non‐frontal facial expression recognition is important in many scenarios. Thus, we propose a new method for facial expression recognition based on AAM‐SIFT and adaptive regional weighting. First, multi‐pose AAM templates are used for pose estimation and feature point location of the facial expression image. For effective and efficient description of these feature points, a hybrid representation, which integrates gradient direction histograms based on the descriptors of scale‐invariant feature transform (SIFT) and AAM, is utilized to form AAM‐SIFT features. Meanwhile, according to different expression regions, AAM‐SIFT features are divided into different groups and the obtained adaptive weights by means of a regional weighted method based on the fuzzy C‐means (FCM) clustering algorithm. Finally, the membership degree computed by FCM, which represents the possibility for each class, is regarded as the input feature vector for support vector machine (SVM) classifier. Extensive experiments on BU‐3DFE database with six facial expressions and seven poses demonstrate the effectiveness of different types of weighting strategies and the influence of different features. Comparison with other state‐of‐art methods illustrates that the proposed method not only improves the recognition rates of the frontal face but also has better robustness for non‐frontal facial expressions. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Bibliography:Key Science and Technology Program of Anhui Province - No. 1206c0805039
Scientific Research Foundation for the Returned Overseas Chinese Scholars
National High-Tech Research & Development Program of China 863 Program - No. 2012AA011103
State Education Ministry
Ministry of Education, Science, Sports and Culture - No. 22240021
ark:/67375/WNG-7SFVDVB9-X
National Natural Science Foundation of China - No. 61432004
ArticleID:TEE22151
istex:BA75BE6B9F595186F8850C0EFED16E7F94C9225F
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1931-4973
1931-4981
DOI:10.1002/tee.22151