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Hand posture and gesture recognition technology

Neural Computing and Applications volume 25, pages 871–878 (2014)Cite this article

Abstract

Hand gestures that are performed by one or two hands can be categorized according to their applications into different categories including conversational, controlling, manipulative and communicative gestures. Generally, hand gesture recognition aims to identify specific human gestures and use them to convey information. The process of hand gesture recognition composes mainly of four stages: hand gesture images collection, gesture image preprocessing using some techniques including edge detection, filtering and normalization, capture the main characteristics of the gesture images and the evaluation (or classification) stage where the image is classified to its corresponding gesture class. There are many methods that have been used in the classification stage of hand gesture recognition such as Artificial Neural Networks, template matching, Hidden Markov Models and Dynamic Time Warping. This exploratory survey aims to provide a progress report on hand posture and gesture recognition technology.

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Authors and Affiliations

  1. Department of Computer Science Artificial Intelligent, University of Malaya, Kuala Lumpur, Malaysia

    Haitham Sabah Badi

  2. Al Yarmouk University College, Baghdad, Iraq

    Sabah Hussein

Authors
  1. Haitham Sabah Badi
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  2. Sabah Hussein
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Correspondence to Haitham Sabah Badi.

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Badi, H.S., Hussein, S. Hand posture and gesture recognition technology. Neural Comput & Applic 25, 871–878 (2014). https://doi.org/10.1007/s00521-014-1574-4

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  • DOI: https://doi.org/10.1007/s00521-014-1574-4

Keywords

  • Gesture recognition
  • Human–computer interaction
  • Representations
  • Natural interfaces
  • Recognition