A Comparative Study of Different Image Features for Hand Gesture Machine Learning

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

trigueiros, p (Author) Other; ribeiro, f (Author) Other; reis, lp (Author) FEUP

Conference proceedings International

Title: ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence

Pages: 51-61

ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence, Volume 2, Barcelona, Spain, 15-18 February, 2013

Indexing

ISI Web of Knowledge

Scopus - 18 Citations

Other information

Authenticus ID: P-008-AZF

Abstract (EN): Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner. We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11