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Facial expressions are the facial changes in response to a person's internal emotional states, intentions, or social communications. Facial expression is a vital part of communication and conveys the emotional state of the individual to observers. Anger, surprise, fear, sadness, happiness and disgust are the six basic prototypic emotions. In this study, emotional face recognition in individuals was investigated. Therefore, automatic emotional face recognition is a great motivating and interesting topic which is related to artificial intelligence, signal processing and pattern recognition topics. Feature extraction and classification are the two main steps in an emotion recognition system. In the current study, the dual tree complex wavelet transform (DT-CWT), and local binary patterns (LBP) are employed as feature extractors with the aim of emotion recognition. In addition, the support vector machines (SVM) and artificial neural network (ANN) were used as classification methods. First, the facial images were processed with DT-CWT and LBP with the aim of feature extraction. Second, Artificial Neural Networks and Support Vector Machines were used for facial expression classification.
About the author
Amina AL-Rahaawi was born in Mosul, Iraq on 06-07-1989. She obtained her BSc in Computer Engineering in 2011 at Technical College of Engineering, University of Mosul, Iraq. At the end of 2013 she worked as lecturer engineer in the same department. After that she got MSc in image processing at Yildiz Technical University, Istanbul, Turkey.
