Real-Time Human Emotion Recognition Using Transfer Learning

The research community has been interested in facial emotion recognition (FER) due to its potential uses. The main task of FER in real-time is mapping distinct face expressions to the corresponding states of emotional. The two main phases of the traditional FER are feature extraction and emotion classification. Due to its built-in feature extraction process from images, Deep Neural Networks “DNN”, particularly a Convolutional Neural Network “CNN”, are being extensively employed in FER. This paper proposed a Deep CNN (DCNN) modeling approach based on the Transfer Learning (TL) for developing a highly precise FER system in real-time in which the fully connected layer is fine-tuned to seven emotion states with using k-nearest neighbor (KNN) and support vector machine (SVM) classifiers instead of SoftMax. The suggested FER system has been validated using the ResNet-101 model and SAVEE image datasets. When used with KNN and SVM classifiers for emotion recognition, the suggested ResNet101 model with SVM classifier model obtains 98.41 % accuracy with respect to other performance measures.