《Image Processing and Machine Learning Techniques for Facial Expression Recognition》.pdf

Image Processing and Machine Learning Techniques for Facial Expression Recognition Anastasios Koutlas Medical Physics Department, Medical School, University of Ioannina,GR 45110, Ioannina, Greece Dimitrios I. Fotiadis Unit of Medical Technology and Intelligent Information Systems, Dept. of Computer Science, University of Ioannina, GR 45110, Ioannina, Greece ABSTRACT The aim of this chapter is to analyse the recent advances in image processing and machine learning techniques with respect to facial expression recognition. A comprehensive review of recently proposed methods is provided along with an analysis of the advantages and the shortcomings of existing systems. Moreover, an example for the automatic identification of basic emotions is presented; Active Shape Models are used to identify prominent features of the face; Gabor filters are used to represent facial geometry at selected locations of fiducial points and Artificial Neural Networks are used for the classification into the basic emotions (anger, surprise, fear, happiness, sadness, disgust, neutral). Finally, the future trends towards automatic facial expression recognition are described. INTRODUCTION The face is the fundamental part of day to day interpersonal communication. Humans use the face along with facial expressions to denote consciously their emotional states (anger, surprise, stress, etc.) or subconsciously (yawn, lip biting), to accompany and enhance the meaning of their thoughts (wink) or exchange thoughts without talking (head nodes, look exchanges). Facial expressions are the result of the deformation in a human’s face due to muscle movement. The importance of automating the task to analyse facial expressions using computing sys