![]() However, the task of recognizing hand gestures in unconstrained real-life scenarios has shown to be longstanding, intractable, and particularly challenging, due to a variety of potential inherent challenges presented by real-world environments, such as partial occlusion, drastic illumination variation, substantial background clutter, extreme hand pose variation, large intra-class variability within each class, changes in scale, and viewpoint and appearance. ![]() ![]() When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.Īutomatic vision-based recognition of hand gestures has recently received a great deal of researchers’ attention in pattern recognition, computer vision, and biometrics communities, due to its potential applicability across a wide variety of applications, ranging from intelligent human-computer interfaces and human-machine communication to machine translation of sign languages of people with severe/profound speech and hearing impairments. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. ![]() In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. Robust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. ![]()
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