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We present an open source cross platform technology for 3D face tracking and analysis. It contains a full stack of components for complete face understanding: detection, head pose tracking, facial expression and action units recognition. Given a depth sensor, one can combine FaceCept3D modules to fulfill a specific application scenario. Key advantages of the technology include real time processing...
Most of the facial expression recognition methods assume frontal or near-frontal head poses and usually their accuracy strongly decreases when tested with non-frontal poses. Training a 2D pose-specific classifier for a large number of discrete poses can be time consuming due to the need of many samples per pose. On the other hand, 2D and 3D view-point independent approaches are usually not robust...
This paper proposes a new framework for head pose estimation under extreme pose variations. By augmenting the precision of a template matching based tracking module with the ability to recover offered by a frame-by-frame head pose estimator, we are able to address pose ranges for which face features are no longer visible, while maintaining state-of-the-art performance. Experimental results obtained...
This paper addresses the problem of Static Hand Gesture Recognition (SHGR) and proposes a fast yet simple solution based on Discrete Hidden Markov Models (DHMMs) that use features extracted from the hand contours. In addition to previous work, the use of depth information ensures robustness to the overall system, making it background invariant. Experiments carried on a challenging noisy dataset reveal...
A real time static isolated gesture recognition application using a Hidden Markov Model approach with features extracted from gestures' silhouettes is presented. Nine different hand poses with various degrees of rotation are considered. The system, both simple and effective, uses color images of the hands to be recognized directly from the camera and is capable of processing 23 frames per second on...
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