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A feature boosted deep learning method for automatic facial expression recognition
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DOI：10.7717/peerj-cs.1216.png)
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Abstract
En 中文
Automatic facial expression recognition (FER) plays a crucial role in human -computer based applications such as psychiatric treatment, classroom assessment, surveillance systems, and many others. However, automatic FER is challenging in real-time environment. The traditional methods used handcrafted methods for FER but mostly failed to produce superior results in the wild environment. In this regard, a deep learning-based FER approach with minimal parameters is proposed, which gives better results for lab-controlled and wild datasets. The method uses features boosting module with skip connections which help to focus on expression-specific features. The proposed approach is applied to FER-2013 (wild dataset), JAFFE (lab -controlled), and CK+ (lab-controlled) datasets which achieve accuracy of 70.21%, 96.16%, and 96.52%. The observed experimental results demonstrate that the proposed method outperforms the other related research concerning accuracy and time.
Keywords:
Facial expression recognition
Convolutional neural networks
Transfer learning
Real-time detection
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