Facial expressions recognition in a single static as well as dynamic facial images using tracking and probabilistic neural networks

Authors:
Hadi Seyedarabi;Won-Sook Lee;Ali Aghagolzadeh;Sohrab Khanmohammadi
Affiliations:
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran;School of Information Technology and Engineering (SITE), Faculty of Engineering, University of Ottawa, Canada;Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran;Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Venue:
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Year:
2006

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Abstract

An efficient, global and local image-processing based extraction and tracking of intransient facial features and automatic recognition of facial expressions from both static and dynamic 2D image/video sequences is presented. Expression classification is based on Facial Action Coding System (FACS) a lower and upper face action units (AUs), and discrimination is performed using Probabilistic Neural Networks (PNN) and a Rule-Based system. For the upper face detection and tracking, we use systems based on a novel two-step active contour tracking system while for the upper face, cross-correlation based tracking system is used to detect and track of Facial Feature Points (FFPs). Extracted FFPs are used to extract some geometric features to form a feature vector which is used to classify input image or image sequences into AUs and basic emotions. Experimental results show robust detection and tracking and reasonable classification where an average recognition rate is 96.11% for six basic emotions in facial image sequences and 94% for five basic emotions in static face images.