Marwa Mahmoud

Publications (4)0 Total impact

• Conference Proceeding: Real-time inference of mental states from facial expressions and upper body gestures.

  Tadas Baltrusaitis, Daniel McDuff, Ntombikayise Banda, Marwa Mahmoud, Rana El Kaliouby, Peter Robinson, Rosalind W. Picard
  Ninth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011), Santa Barbara, CA, USA, 21-25 March 2011; 01/2011
• Source

  Available from: cl.cam.ac.uk

  Conference Proceeding: Interpreting Hand-Over-Face Gestures.

  Marwa Mahmoud, Peter Robinson
  Affective Computing and Intelligent Interaction - Fourth International Conference, ACII 2011, Memphis, TN, USA, October 9-12, 2011, Proceedings, Part II; 01/2011
• Source

  Available from: cam.ac.uk

  Conference Proceeding: 3D Corpus of Spontaneous Complex Mental States.

  Marwa Mahmoud, Tadas Baltrusaitis, Peter Robinson, Laurel D. Riek
  Affective Computing and Intelligent Interaction - 4th International Conference, ACII 2011, Memphis, TN, USA, October 9-12, 2011, Proceedings, Part I; 01/2011
• Chapter: Towards Communicative Face Occlusions: Machine Detection of Hand-over-Face Gestures

  Marwa Mahmoud, Rana El-Kaliouby, Amr Goneid
  [show abstract] [hide abstract]
  ABSTRACT: Emotional body language constitutes an important channel of non-verbal information. Of this large set, hand-over-face gestures are treated as noise because they occlude facial expressions. In this paper, we propose an alternative facial processing framework where face occlusions instead of being removed, are detected, localized and eventually classified into communicative gestures. We present a video corpus of hand-over-face gestures and describe a multi-stage methodology for detecting and localizing these gestures. For pre-processing, we show that force fields form a better representation of images compared to edge detectors. For feature extraction, detection and localization, we show that Local Binary Patterns outperform Gabor filters in accuracy and speed. Our methodology yields an average detection rate of 97%, is robust to changes in facial expressions, hand shapes, and limited head motion, and preliminary testing with spontaneous videos suggests that it may generalize successfully to naturally evoked videos.
  07/2009: pages 481-490;