Feature extraction techniques for human emotion identification from face images

Emotion recognition has been one of the stimulating issues over the years due to the irregularities in the complexity of models and unpredictability between expression categories. So many Emotion detection algorithms have developed in the last two decades and still facing problems in accuracy, complexity and real-world implementation. In this paper, we propose two feature extraction techniques: Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method. In Mouth based feature extraction method mouth area is calculated and based on that value the emotions are classified. In the MSER method, the features are extracted by using connecting components and then the extracted features are given to a simple ANN for classification. Experimental results shows that the Mouth area based feature extraction method gives 86% accuracy and MSER based feature extraction method outperforms it by achieving 89% accuracy on DEAP. Thus, it can be concluded that the proposed methods can be effectively used for emotion detection.