Marwa Mahmoud

University of Cambridge, Cambridge, England, United Kingdom

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Publications (5)0 Total impact

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    ABSTRACT: Prolonged durations of rhythmic body gestures were proved to be correlated with different types of psychological disorders. To-date, there is no automatic descriptor that can robustly detect those behaviours. In this paper, we propose a cyclic gestures descriptor that can detect and localise rhythmic body movements by taking advantage of both colour and depth modalities. We show experimentally how our rhythmic descriptor can successfully localise the rhythmic gestures as: hands fidgeting, legs fidgeting or rocking, significantly higher than the majority vote classification baseline. Our experiments also demonstrate the importance of fusing both modalities, with a significant increase in performance when compared to individual modalities.
    Proceedings of the 15th ACM on International conference on multimodal interaction; 12/2013
  • Marwa M. Mahmoud, Tadas Baltrusaitis, Peter Robinson
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    ABSTRACT: Crowdsourcing is becoming increasingly popular as a cheap and effective tool for multimedia annotation. However, the idea is not new, and can be traced back to Charles Darwin. He was interested in studying the universality of facial expressions in conveying emotions, thus he had to consider a global population. Access to different cultures allowed him to reach more general conclusions. In this paper, we highlight a few milestones in the history of the study of emotion that share the concepts of crowdsourcing. We first consider the study of posed photographs and then move to videos of natural expressions. We present our use of crowdsouring to label a video corpus of natural expressions, and also to recreate one of Darwin's original emotion judgment experiments. This allows us to compare people's perception of emotional expressions in the 19th and 21st centuries, showing that it remains stable through both culture and time.
    Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia; 10/2012
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    ABSTRACT: We present a real-time system for detecting facial action units and inferring emotional states from head and shoulder gestures and facial expressions. The dynamic system uses three levels of inference on progressively longer time scales. Firstly, facial action units and head orientation are identified from 22 feature points and Gabor filters. Secondly, Hidden Markov Models are used to classify sequences of actions into head and shoulder gestures. Finally, a multi level Dynamic Bayesian Network is used to model the unfolding emotional state based on probabilities of different gestures. The most probable state over a given video clip is chosen as the label for that clip. The average F1 score for 12 action units (AUs 1, 2, 4, 6, 7, 10, 12, 15, 17, 18, 25, 26), labelled on a frame by frame basis, was 0.461. The average classification rate for five emotional states (anger, fear, joy, relief, sadness) was 0.440. Sadness had the greatest rate, 0.64, anger the smallest, 0.11.
    Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on; 04/2011
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    ABSTRACT: Hand-over-face gestures, a subset of emotional body language, are overlooked by automatic affect inference systems. We propose the use of hand-over-face gestures as a novel affect cue for automatic inference of cognitive mental states. Moreover, affect recognition systems rely on the existence of publicly available datasets, often the approach is only as good as the data. We present the collection and annotation methodology of a 3D multimodal corpus of 108 audio/video segments of natural complex mental states. The corpus includes spontaneous facial expressions and hand gestures labelled using crowd-sourcing and is publicly available.
    Affective Computing and Intelligent Interaction - 4th International Conference, ACII 2011, Memphis, TN, USA, October 9-12, 2011, Proceedings, Part I; 01/2011
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    Marwa Mahmoud, Peter Robinson
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    ABSTRACT: People often hold their hands near their faces as a gesture in natural conversation, which can interfere with affective inference from facial expressions. However, these gestures are valuable as an additional channel for multi-modal inference. We analyse hand-over-face gestures in a corpus of naturalistic labelled expressions and propose the use of those gestures as a novel affect cue for automatic inference of cognitive mental states. We define three hand cues for encoding hand-over-face gestures, namely hand shape, hand action and facial region occluded, serving as a first step in automating the interpretation process.
    Affective Computing and Intelligent Interaction - Fourth International Conference, ACII 2011, Memphis, TN, USA, October 9-12, 2011, Proceedings, Part II; 01/2011