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Facial expression recognition based on AAM–SIFT and adaptive regional weighting
Author(s) -
Ren Fuji,
Huang Zhong
Publication year - 2015
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22151
Subject(s) - artificial intelligence , weighting , pattern recognition (psychology) , scale invariant feature transform , computer science , support vector machine , facial expression , active appearance model , classifier (uml) , histogram , robustness (evolution) , facial recognition system , cluster analysis , feature extraction , feature (linguistics) , computer vision , image (mathematics) , medicine , biochemistry , chemistry , linguistics , philosophy , gene , radiology
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.