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What is Feature Vector Extraction

Chapter 16
Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
The task of providing a feature vector that describes facial geometry and deformation. There are two ways to model facial geometry and deformation, first by using prominent features of the face and second by using a mathematical transformation so that changes in appearance are modeled
Published in Chapter:
Image Processing and Machine Learning Techniques for Facial Expression Recognition
Anastasios Koutlas (University of Ioannina, Greece) and Dimitrios I. Fotiadis (University of Ioannina, Greece)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/978-1-60566-314-2.ch016
Abstract
The aim of this chapter is to analyze 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); and finally, the future trends towards automatic facial expression recognition are described.
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