Facial expression recognition with trade-offs between data augmentation and deep learning features

A novel facial expression recognition system has been proposed in this paper. The objective of this paper is to recognize the types of expressions in the human face region. The implementation of the proposed system has been divided into four components. In the first component, a region of interest as face detection has been performed from the captured input image. For extracting more distinctive and discriminant features, in the second component, a deep learning-based convolutional neural network architecture has been proposed to perform feature learning tasks for classification purposes to recognize the types of expressions. To enhance the performance of the proposed system, in the third component, some novel data augmentation techniques have been applied to the facial image to enrich the learning parameters of the proposed CNN model. In the fourth component, a trade-off between data augmentation and deep learning features have been performed for fine-tuning the trained CNN model. Extensive experimental results have been demonstrated using three benchmark databases: KDEF (seven expression classes), GENKI-4k (two expression classes), and CK+ (seven expression classes). The performance of the proposed system respect for each database has been well presented and described and finally, these performances have been compared with the existing state-of-the-art methods. The comparison with competing methods shows the superiority of the proposed system.