Research Article Open Access

Fusion of Features and Extreme Learning Machine for Facial Expression Recognition

Bayezid Islam1 and Arfat Hossain1
  • 1 Rajshahi University of Engineering and Technology, Bangladesh
Journal of Computer Science
Volume 15 No. 12, 2019, 1833-1841


Submitted On: 4 April 2019 Published On: 28 December 2019

How to Cite: Islam, B. & Hossain, A. (2019). Fusion of Features and Extreme Learning Machine for Facial Expression Recognition. Journal of Computer Science, 15(12), 1833-1841.


Human emotion is highly correlated to facial expressions. Due to its growing demand in different sectors, an emotion recognition method is proposed through recognizing facial expressions. The input image is preprocessed and then the resulting image is segmented into four facial expression regions following the newly proposed segmentation method. Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are fused to extract the necessary features from the four segmented parts. The dimension of the feature vector is reduced using Principal Component Analysis (PCA). To classify the expressions, Extreme Learning Machine (ELM) is used. For evaluating the performance of the proposed method, three widely used and publicly available facial expression datasets (JAFFE, CK+, RaFD) are used. The proposed method achieved 95.3%, 99.84% and 98.65% accuracy while using images from JAFFE, CK+ and RaFD dataset respectively. Performance of the proposed method on these datasets is compared to other facial expression recognition methods on these datasets to indicate that the proposed method achieves state-of-the-art performance. 

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  • Facial Region Segmentation
  • Fusion of HOG and LBP
  • Principal Component Analysis (PCA)
  • Extreme Learning Machine (ELM)