Journal
INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE
Volume 11, Issue 1, Pages 48-70Publisher
IGI GLOBAL
DOI: 10.4018/IJACI.2020010103
Keywords
Artificial Neural Networks; Facial Emotion Recognition; Feature Fusion; Gabor Wavelets; Local Binary Patterns; Local Directionality Patterns; Machine Vision; Support Vector Machines
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Facial expression analysis and recognition has gained popularity in the last few years for its challenging nature and broad area of applications like HCI, pain detection, operator fatigue detection, surveillance, etc. The key of real-time FER system is exploiting its variety of features extracted from the source image. In this article, three different features viz. local binary pattern, Gabor, and local directionality pattern were exploited to perform feature fusion and two classification algorithms viz. support vector machines and artificial neural networks were used to validate the proposed model on benchmark datasets. The classification accuracy has been improved in the proposed feature fusion of Gabor and LDP features with SVM classifier, recorded an average accuracy of 93.83% on JAFFE, 95.83% on CK and 96.50% on MMI. The recognition rates were compared with the existing studies in the literature and found that the proposed feature fusion model has improved the performance.
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