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Sensors 2016, 16(1), 36;

Real-Time Hand Posture Recognition for Human-Robot Interaction Tasks

Universidad de Guanajuato DICIS, Carr. Salamanca-Valle Km. 3.5 + 1.8, Palo Blanco, Salamanca, C.P. 36885, Mexico
Author to whom correspondence should be addressed.
Academic Editor: Lianqing Liu
Received: 30 October 2015 / Revised: 9 December 2015 / Accepted: 18 December 2015 / Published: 4 January 2016
(This article belongs to the Special Issue Sensors for Robots)
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In this work, we present a multiclass hand posture classifier useful for human-robot interaction tasks. The proposed system is based exclusively on visual sensors, and it achieves a real-time performance, whilst detecting and recognizing an alphabet of four hand postures. The proposed approach is based on the real-time deformable detector, a boosting trained classifier. We describe a methodology to design the ensemble of real-time deformable detectors (one for each hand posture that can be classified). Given the lack of standard procedures for performance evaluation, we also propose the use of full image evaluation for this purpose. Such an evaluation methodology provides us with a more realistic estimation of the performance of the method. We have measured the performance of the proposed system and compared it to the one obtained by using only the sampled window approach. We present detailed results of such tests using a benchmark dataset. Our results show that the system can operate in real time at about a 10-fps frame rate. View Full-Text
Keywords: human-robot interaction; vision-based hand posture recognition; full image evaluation; AdaBoost; bootstrapping human-robot interaction; vision-based hand posture recognition; full image evaluation; AdaBoost; bootstrapping

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Hernandez-Belmonte, U.H.; Ayala-Ramirez, V. Real-Time Hand Posture Recognition for Human-Robot Interaction Tasks. Sensors 2016, 16, 36.

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