ELCVIA Electronic Letters on Computer Vision and Image Analysis https://elcvia.cvc.uab.es/ Electronic Journal on Computer Vision and Image Analysis CVC Press en-US ELCVIA Electronic Letters on Computer Vision and Image Analysis 1577-5097 Authors who publish with this journal agree to the following terms:<br /><ol type="a"><li>Authors retain copyright.</li><li>The texts published in this journal are – unless indicated otherwise – covered by the Creative Commons Spain <a href="http://creativecommons.org/licenses/by-nc-nd/4.0">Attribution-NonComercial-NoDerivatives 4.0</a> licence. You may copy, distribute, transmit and adapt the work, provided you attribute it (authorship, journal name, publisher) in the manner specified by the author(s) or licensor(s). The full text of the licence can be consulted here: <a href="http://creativecommons.org/licenses/by-nc-nd/4.0">http://creativecommons.org/licenses/by-nc-nd/4.0</a>.</li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li></ol> Modelling and Analysis of Facial Expressions Using Optical Flow Derived Divergence and Curl Templates https://elcvia.cvc.uab.es/article/view/v20-n2-anthwal Facial expressions are integral part of non-verbal paralinguistic communication as they provide cues significant in perceiving one’s emotional state. Assessment of emotions through expressions is an active research domain in computer vision due to its potential applications in multi-faceted domains. In this work, an approach is presented where facial expressions are modelled and analyzed with dense optical flow derived divergence and curl templates that embody the ideal motion pattern of facial features pertaining to unfolding of an expression on the face. Two types of classification schemes based on multi-class support vector machine and k-nearest neighbour are employed for evaluation. Promising results obtained from comparative analysis of the proposed approach with state-of-the-art techniques on the Extended Cohn Kanade database and with human cognition and pre-trained Microsoft face application programming interface on the Karolinska Directed Emotional Faces database validate the efficiency of the approach. Shivangi Anthwal Copyright (c) 2021 Shivangi Anthwal https://creativecommons.org/licenses/by-nc-nd/4.0 2021-06-01 2021-06-01 20 2 1 21 10.5565/rev/elcvia.1275