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Facial Expression Recognition using Spatial Feature Extraction and Ensemble Deep Networks
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Authors
1Bu-ali Sina University (08138344961)
2Computer Engineering Department Bu-Ali Sina University Hamedan, Iran
3Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran
Abstract
Researchers have shown that 55% of concepts are conveyed through facial emotion and only 7% are conveyed by words and sentences, so facial expression plays an important role in conveying concepts in human communications. In recent years, due to the improvement of artificial neural networks, many studies have been conducted related to facial expression recognition. This paper presents a method based on ensemble classification using convolutional neural networks to recognize facial emotions. The concatenation of spatial features with global features is used as a feature map for the classification stage in the committee network. Two committee networks are fed separately with LBP and raw images. After training the two committee networks, to classify the emotion, the maximum probability between the two networks is considered as the final output. The proposed method was applied and tested on the FER2013 dataset. Our proposed method is more accurate than many leading methods, and in competition with the successful model that has a more complex architecture and higher computational cost, it has been able to achieve acceptable results with a simple architecture.
Keywords
 
Proceeding Title [Persian]
Facial Expression Recognition using Spatial Feature Extraction and Ensemble Deep Networks
Authors [Persian]
Abstract [Persian]
Researchers have shown that 55% of concepts are conveyed through facial emotion and only 7% are conveyed by words and sentences, so facial expression plays an important role in conveying concepts in human communications. In recent years, due to the improvement of artificial neural networks, many studies have been conducted related to facial expression recognition. This paper presents a method based on ensemble classification using convolutional neural networks to recognize facial emotions. The concatenation of spatial features with global features is used as a feature map for the classification stage in the committee network. Two committee networks are fed separately with LBP and raw images. After training the two committee networks, to classify the emotion, the maximum probability between the two networks is considered as the final output. The proposed method was applied and tested on the FER2013 dataset. Our proposed method is more accurate than many leading methods, and in competition with the successful model that has a more complex architecture and higher computational cost, it has been able to achieve acceptable results with a simple architecture.
Keywords [Persian]
facial expression recognition، local binary pattern، image spatial features، ensemble networks، Deep Learning