Facial expression recognition using multi Radial Bases Function Networks and 2-D Gabor filters

Facial expression analysis and recognition have been researched since the 17'th century. The foundational studies on facial expressions, which have formed the basis of today's research, can be traced back to few centuries ago. Precisely, a detailed note on the various expressions and movements of head muscles was given in 1649 by John Bulwer(1). Another important milestone in the study of facial expressions and human emotions, is the work done by the psychologist Paul Ekman(2) and his colleagues. This important work have been done in the 1970s and has a significant importance and large influence on the development of modern day automatic facial expression recognizers. This work lead to adapting and developing the comprehensive Facial Action Coding System(FACS), which has since then become the de-facto standard for facial expression recognition. Over the last decades, automatic facial expressions analysis has become an active research area that finds potential applications in fields such as Human-Computer Interfaces (HCI), Image Retrieval, Security and Human Emotion Analysis. Facial expressions are extremely important in any human interaction, and additional to emotions, it also reflects on other mental activities, social interaction and physiological signals. In this paper, we proposes an Artificial Neural Network (ANN) of two hidden layers, based on multiple Radial Bases Functions Networks (RBFN's) to recognize facial expressions. The ANN, is trained on features extracted from images by applying a multi-scale and multi-orientation Gabor filters. We have considered the cases of subject independent/dependent facial expression recognition using The JAFFE and the CK+ benchmarks to evaluate the proposed model.