EFL-LCNN: Enhanced face localization augmented light convolutional neural network for human emotion recognition

Facial expression is an inevitable aspect of human communication, and hence facial emotion recognition (FER) has become the basis for many machine vision applications. Many deep learning based FER models have been developed and shown good results on emotion recognition. However, FER using deep learning still suffering from illumination conditions, noise around the face such as hair, background, and other ambience conditions. To mitigate such issues and improve the performance of FER, we propose Enhanced Face Localization augmented Light Convolution Neural Network (EFL-LCNN). EFL-LCNN incorporates three phase pre-processing and Light CNN, a trimmed VGG16 model. Three phase pre-processing includes face detection, enhanced face region cropping for ambience noise removal and image enhancement using CLAHE for addressing illumination problems. Three phase pre-processing is followed by the implementation of Light CNN to improve FER performance with reduced complexity. The EFL-LCNN is rigorously tested on four publicly available benchmark datasets: JAFFE, CK, MUG and KDEF. It is observed from the empirical results that the EFL-LCNN boosted recognition accuracies significantly when compared with the state-of-the-art.