HMM based hand gesture recognition: A review on techniques and approaches

Moni, Mohammad Ali; Ali, A. B. M. Shawkat

doi:10.1109/iccsit.2009.5234536

Cited by 54 publications

(25 citation statements)

References 8 publications

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“…Hidden markov models (HMMs) have been successfully applied in speech recognition and hand gesture recognition [50] [51]. HMMs represent different phases of a gait as hidden states.…”

Section: State-space Model: Hmmmentioning

confidence: 99%

A Review of Vision-Based Gait Recognition Methods for Human Identification

Wang

She

Nahavandi

et al. 2010

2010 International Conference on Digital Image Computing: Techniques and Applications

168

108

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Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field.

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“…Hidden markov models (HMMs) have been successfully applied in speech recognition and hand gesture recognition [50] [51]. HMMs represent different phases of a gait as hidden states.…”

Section: State-space Model: Hmmmentioning

confidence: 99%

A Review of Vision-Based Gait Recognition Methods for Human Identification

Wang

She

Nahavandi

et al. 2010

2010 International Conference on Digital Image Computing: Techniques and Applications

168

108

View full text Add to dashboard Cite

show abstract

“…T. E. Stanner have employed HMM in 1995 in identifying the American Sign language. On similar grounds authors Gaus Y. F. A. et al have successfully recognized the Malaysian Sign Language [5], skin segmentation procedure throughout frames and feature extraction by centroids, hand distances and orientation has been used, gesture paths define the hand trajectory. Kalaman filters have been used by researchers to identify overlaping hand-head and hand-hand regions.…”

Section: History and Literature Surveymentioning

confidence: 99%

Hand Gesture Recognition using Colour Based Segmentation and Hidden Markov Model

Deo¹,

Girolkar²,

Kulkarni³

2015

IJCATR

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Automatic gesture recognition is a key technology used in Human Computer Interaction. In this paper we introduce a hand gesture recognition system which consists of 4 modules, image segmentation, feature extraction, HMM training and gesture recognition. Image or video is divided into multiple frames and segmentation process which uses colour based detection of the path of the object is applied to each frame. Feature extraction process mainly considers the orientation of the state tracked. This is done using HSV[hue-saturation value] image and contour mapping of the image. The training part of the HMM model works on basis of LRB(Left-Right-Banded)topology and uses the BW (Baum Welch) algorithm. We have used Viterbi algorithm for mapping the state to a symbol i.e. recognition. HMM is used to predict the gesture and increase the tolerance of the system to incorporate human errors.

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“…Several learning based methods have been reported to be successful for hand gesture recognition such as Hidden Markov Models [3], neural networks or Haar-like features [4]. These methods rely on the identification of the canonical description of hand gesture.…”

Section: Introductionmentioning

confidence: 99%