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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Signal processingSubject: search.filters.subject.Feature ExtractionAuthor: search.filters.author.Ulukaya, Sezer

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    A comparison of geometrical facial features for affect recognition, Duygu tanima i̇çi̇n geometri̇k yüz özni̇teli̇kleri̇ni̇n karşilaştirilmasi
    (2011) Ulukaya, Sezer; Erdem, Cigdem Eroglu; Ulukaya, Sezer, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, Turkey
    In this work, we compare two different geometric feature extraction methods derived from coordinates of facial points tracked by Active Appearance Models. The compared feature extraction methods differ in their use of coordinates or distances between facial points and whether they use the information of a neutral facial expression. Experiments on the extended Cohn-Kanade database show that the coordinate-based features using the neutral frame information gives the best emotion recognition results (%94) using a SVC classifier with a polynomial kernel. © 2011 IEEE. © 2011 Elsevier B.V., All rights reserved.
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    Estimation of the neutral face shape using Gaussian mixture models
    (2012) Ulukaya, Sezer; Erdem, Cigdem Eroglu; Ulukaya, Sezer, Department of Electrical and Electronic Engineering, Boğaziçi Üniversitesi, Bebek, Turkey, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey
    We present a Gaussian Mixture Model (GMM) fitting method for estimating the unknown neutral face shape for frontal facial expression recognition using geometrical features. Subtracting the estimated neutral face, which is related to the identity-specific component of the shape leaves us with the component related to the variations resulting from facial expressions. Experimental results on the Extended Cohn-Kanade (CK+) database show that subtracting the estimated neutral face shape gives better emotion recognition rates as compared to classifying the geometrical facial features directly, when the person-specific neutral face shape is not available. We also experimentally evaluate two different geometric facial feature extraction methods for emotion recognition. The average emotion recognition rates achieved with the proposed neutral shape estimation method and coordinate based features is 88%, which is higher than the baseline results presented in the literature, although we do not use the person-specific neutral shapes (94% if we use), and any appearance based features. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.