A combination of user-guide scheme and kernel descriptor on RGB-D data for robust and realtime hand posture recognition. (March 2016)

• Record Type: Journal Article
• Authors: Doan, Huong-Giang; Nguyen, Van-Toi; Vu, Hai; Tran, Thanh-Hai
• Is Part Of: Engineering applications of artificial intelligence. Volume 49(2016:Jan.)
• Page Start: 103
• Page End: 113
• Publication Date: 2016-03
• Subjects: Background subtractions -- Human computer interaction -- User-guide learning scheme -- Hand gesture recognition -- Color skin model; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.11.010
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents a robust and real-time hand posture recognition system. To obtain this, key elements of the proposed system contain an user-guide scheme and a kernel-based hand posture representation. We firstly describe a three-stage scheme to train an end-user. This scheme aims to adapt environmental conditions (e.g., background images, distance from device to hand/human body) as well as to learn appearance-based features such as hand-skin color. Thanks to the proposed user-guide scheme, we could precisely estimate heuristic parameters which play an important role for detecting and segmenting hand regions. Based on the segmented hand regions, we utilize a kernel-based hand representation in which three levels of feature are extracted. Whereas pixel-level and patch-level are conventional extractions, we construct image-level which presents a hand pyramid structure. These representations contribute to a Multi-class support vector machine classifier. We evaluate the proposed system in term of the learning time versus the robustness and real time performances. Averagely, the proposed system requires 14 s in advanced to guide an end-user. However, the hand posture recognition rate obtains 91.2% accuracy. Performance of the proposed system is comparable with state-of-the-art methods (e.g.Pisharady et al., 2012 ) but it is a real time system. To recognize a posture, its computational cost is only 0.15 s. This is significantly faster than works inPisharady et al. (2012), which required approximately 2 min. The proposed methods therefore are feasible to embed into smart devices, particularly, consumer electronics in domain of home-automation such as televisions, game consoles, or lighting systems.