Abstract
The automatic interpretation of human gestures can be used for a natural interaction with computers while getting rid of mechanical devices such as keyboards and mice. In order to achieve this objective, the recognition of hand postures has been studied for many years. However, most of the literature in this area has considered 2D images which cannot provide a full description of the hand gestures. In addition, a rotation-invariant identification remains an unsolved problem, even with the use of 2D images. The objective of the current study was to design a rotation-invariant recognition process while using a 3D signature for classifying hand postures. A heuristic and voxel-based signature has been designed and implemented. The tracking of the hand motion is achieved with the Kalman filter. A unique training image per posture is used in the supervised classification. The designed recognition process, the tracking procedure and the segmentation algorithm have been successfully evaluated. This study has demonstrated the efficiency of the proposed rotation invariant 3D hand posture signature which leads to 93.88% recognition rate after testing 14,732 samples of 12 postures taken from the alphabet of the American Sign Language.
Highlights
Background and ObjectiveInteractions between humans and computers are typically carried out using keyboards, mice and joysticks
The objective of the current study is to design a range camera based system where a high number of postures taken from the alphabet of the American Sign Language can be recognized in real-time
The range camera with its ability to collect at video rates has been used to capture the scene where the user performing one posture at a time was moving his hand in all three directions of the camera frame and rotating his hand
Summary
Background and ObjectiveInteractions between humans and computers are typically carried out using keyboards, mice and joysticks. Though very high recognition rates are usually claimed by authors who have used a variety of techniques (100% for [1], 98.6% for [2], 98% for [3]), hand gesture recognition remains a timely research topic with many unresolved problems. This can be seen when taking into account the high number of papers written on the topic in 2012: more than 60 were found using only the Compendex database with a query made on 16 April 2012.
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