Facial micro-expression classification through an optimized convolutional neural network using genetic algorithm

Abstract

Computer vision facilitates machines to interpret the visual world using various computer aided detection (CAD)-based techniques. It plays a crucial role in micro-expression auto classification. A micro-expression is a brief facial movement which reveals a genuine emotion that a person tries to conceal, it usually lasts for a short duration and is imperceptible with normal vision. To reveal people’s genuine emotions, an automatic micro-expression screening using convolutional neural network (CNN) is in great need. Traditional methods for micro-expression recognition (MER) suffer from low classification accuracy due to inadequate CNN hyperparameters selection. The proposed approach addresses these challenges by using an optimized CNN with adequate learning rate, batch size, epochs, and dropout rate. Real-coded genetic algorithm (RCGA) has been employed for the hyperparameter optimization. In this experimentation, features are extracted from the onset and apex frames of microexpression video clips of CASME II dataset. The proposed model's performance is measured using various metrics, including accuracy, precision, and recall. The proposed approach’s performance is then compared with an optimized CNN using random search algorithm. The empirical investigation of existing CNN-based methods has proven efficacy of our proposed model.