Notice of Violation of IEEE Publication Principles: Advanced recognition techniques for human computer interaction

Notice of Violation of IEEE Publication Principles

"Advanced Recognition Techniques for Human Computer Interaction"
by Chetan A. Burande, Raju M. Tugnayat, Nitin K. Choudhary
in the Proceedings of the 2nd International Conference on Computer and Automation Engineering (ICCAE), February 2010, pp. 480-483

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains substantial duplication of text and figures from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Hand Gesture Recognition for Human-Machine Interaction"
by Elena Sanchez-Nielsen (1), Luis Anton-Canalis (1), Mario Hernandez-Tejera (2)
in the Journal of WSCG, Vol 12, No. I-3, February 2003, pp. 395-402


Hand gestures are an important modality for human computer interaction (HCI). Compared to many existing interfaces, hand gestures have the advantages of being easy to use, natural, and intuitive. Successful applications of hand gesture recognition include computer games control, human-robot interaction, and sign language recognition, to name a few. Vision-based recognition systems can give computers the capability of understanding and responding to hand gestures. The aim of this technique is the proposal of a real time vision system for its application within visual interaction environments through hand gesture recognition, using general-purpose hardware and low cost sensors, like a simple personal computer and an USB web cam, so any user could make use of it in his office or home. The basis of our approach is a fast segmentation process to obtain the moving hand from the whole image, which is able to deal with a large number of hand shapes against different backgrounds and lighting conditions, and a recognition process that identifies the hand posture from the temporal sequence of segmented hands. The use of a visual memory (Stored database) allows the system to handle variations within a gesture and speed up the recognition process through the storage of different variables related to each gesture. A hierarchical gesture recognition algorithm is introduced to recognize a large number of gestures. Three stages of the proposed algorithm are based on a new hand tracking technique to recognize the actual beginning of a gesture using a Kalman filtering process, hidden Markov models and graph matching. Processing time is important in working with large databases. Therefore, special cares are taken to deal with the large number of gestures.