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.
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Seyedarabi, H., Lee, WS., Aghagolzadeh, A., Khanmohammadi, S. (2006). Facial Expressions Recognition in a Single Static as well as Dynamic Facial Images Using Tracking and Probabilistic Neural Networks. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_29
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DOI: https://doi.org/10.1007/11949534_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
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