Deep Hybrid Neural Networks for Facial Expression Classification

Aakash Babasaheb Jadhav, Sairaj Laxman Burewar, Ajay Ashokrao Waghumbare, Anil Balaji Gonde

Abstract: Facial Expression evaluation has become necessary for human machine interaction, behavior analysis and also forensic and clinical evaluation. Deep convolutional neural networks (CNN) have been largely used for facial expression recognition but due to locality of convolution, CNNs results in lower accuracy when trained with facial expression data of varying ethnicity and emotion intensity. Recurrent neural networks (RNN) are used to work with sequential data and used to predict the sequences. We propose CNN-RNN network approach, a hybrid network, wherein the outputs from CNN and RNN have been concatenated to predict the final emotion, similarly a CNN model followed by a RNN layers has been designed that gives promising results. The proposed hybrid models are evaluated on two publically available datasets CK+ and JAFFE which provide us with variation in ethnicity and emotion intensity. Promising results have been obtained with this hybrid approach when compared to various machine learning and deep learning methods.

External IDs:dblp:conf/cvip/JadhavBWG19