A New Algorithm to Classify Face Emotions through Eye and Lip Features by Using Particle Swarm Optimization
Ahmad Habibizad Navin, Mir Kamal Mirnia · 2012
Facial expressions give important clues about emotions. Computer systems based on affective interaction could play an important role in the next generation of biometric surveillance systems. Face emotion recognition is one of the main applications of computer vision that is widely attended in recent years and can be used in areas of security, entertainment and human machine interface (HMI). The research of emotion recognition consists of facial expressions, vocal, gesture and physiology signal recognition and etc. In this paper a new algorithm based on a set of images to face emotion recognition has been proposed. This process involves three stages: pre-processing, feature extraction and classification. Firstly a series of pre- processing tasks such as adjusting contrast, filtering, skin color segmentation and edge detection are done. One of the important tasks at this stage after pre- processing is to extract features. To extract features with high speed projection profile is used. Second particle swarm optimization (PSO) is used to optimize eye and lip ellipse characteristics. Finally in the third stage, with using the features obtained of the optimal ellipse eye and lip, a person emotion according to experimental results and emotions represented by Ekman (sadness, angry, joy, fear, disgust and surprise without consider natural emotion) is classified. The obtained results show that the success rate and running speed in face emotion recognition in comparison with previous methods has better performance.