Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking Articulated Hand Motion with Eigen Dynamics Analysis
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Resolving hand over face occlusion
Image and Vision Computing
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.