Researcher:
Marzban, Shabbir

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Master Student

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Shabbir

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Marzban

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Marzban, Shabbir

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2014 - 20152

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Researcher Of Publications: search.filters.isAuthorOfPublication.a21f29dc-6798-4c94-b4e3-dc06f30a7462

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    Affect burst detection using multi-modal cues
    (IEEE, 2015) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; N/A; Sezgin, Tevfik Metin; Yemez, Yücel; Türker, Bekir Berker; Erzin, Engin; Marzban, Shabbir; Faculty Member; Faculty Member; PhD Student; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; 18632; 107907; N/A; 34503; N/A
    Recently, affect bursts have gained significant importance in the field of emotion recognition since they can serve as prior in recognising underlying affect bursts. In this paper we propose a data driven approach for detecting affect bursts using multimodal streams of input such as audio and facial landmark points. The proposed Gaussian Mixture Model based method learns each modality independently followed by combining the probabilistic outputs to form a decision. This gives us an edge over feature fusion based methods as it allows us to handle events when one of the modalities is too noisy or not available. We demonstrate robustness of the proposed approach on 'Interactive emotional dyadic motion capture database' (IEMOCAP) which contains realistic and natural dyadic conversations. This database is annotated by three annotators to segment and label affect bursts to be used for training and testing purposes. We also present performance comparison between SVM based methods and GMM based methods for the same configuration of experiments.
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    Affect burst recognition using multi-modal cues
    (IEEE Computer Society, 2014) N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Türker, Bekir Berker; Marzban, Shabbir; Erzin, Engin; Yemez, Yücel; Sezgin, Tevfik Metin; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 34503; 107907; 18632
    Affect bursts, which are nonverbal expressions of emotions in conversations, play a critical role in analyzing affective states. Although there exist a number of methods on affect burst detection and recognition using only audio information, little effort has been spent for combining cues in a multi-modal setup. We suggest that facial gestures constitute a key component to characterize affect bursts, and hence have potential for more robust affect burst detection and recognition. We take a data-driven approach to characterize affect bursts using Hidden Markov Models (HMM), and employ a multimodal decision fusion scheme that combines cues from audio and facial gestures for classification of affect bursts. We demonstrate the contribution of facial gestures to affect burst recognition by conducting experiments on an audiovisual database which comprise speech and facial motion data belonging to various dyadic conversations.