Recognizing Facial Expressions: A Comparison of Computational Approaches

• Published in: Artificial Neural Networks - ICANN 2008 pp. 1001 - 1010
• Main Authors: Shenoy, Aruna, Gale, Tim M., Davey, Neil, Christiansen, Bruce, Frank, Ray
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Classification; Dimensionality Reduction; Facial Expressions; Image Analysis
• ISBN: 9783540875352; 3540875352
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-87536-9_102

Recognizing facial expressions are a key part of human social interaction,and processing of facial expression information is largely automatic, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high dimensional data, so here we use some dimensionality reduction techniques: Linear Discriminant Analysis, Principal Component Analysis and Curvilinear Component Analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images are identified. Subsequently the faces are classified using a Support Vector Machine. We show that it is possible to differentiate faces with a neutral expression from those with a smiling expression with high accuracy. Moreover we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 11 dimensions.