Study on Techniques of Hand Gesture Recognition

2012 ◽  
Vol 241-244 ◽  
pp. 1664-1667
Author(s):  
Shou Fang Mi ◽  
Ling Hua Li
Keyword(s):  
Feature Extraction ◽  
Gesture Recognition ◽  
Dynamic Bayesian Network ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Time Warping ◽  
Language Interpretation ◽  
Dynamic Time ◽  
Main Ideas

This paper describes the study of techniques used in hand gesture recognition in sign language interpretation. The study is discussed from two aspects: the process of hand gesture recognition and the techniques of feature extraction in hand gesture recognition. The techniques of feature extraction in hand gesture recognition are grouped into five categories: Hidden Markov Model (HMM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Dynamic Bayesian Network (DBN), and Dynamic Time Warping (DTW). The main ideas and the application of each technique are described in detail.

Visual Based Hand Gesture Recognition Systems

2012 ◽  
Vol 263-266 ◽  
pp. 2422-2425
Author(s):  
Ling Hua Li ◽  
Ji Fang Du
Keyword(s):  
Feature Extraction ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Appearance Model ◽  
Hand Gesture ◽  
Model Based ◽  
Vector Machines ◽  
Recognition Systems ◽  
Main Ideas

This paper describes the techniques used in visual based hand gesture recognition systems. The study is discussed from three aspects: the two categories, the five components, and the methods of feature extraction of visual based hand gesture recognition systems. The two categories are 3D model based systems and appearance model based systems. The five components are image sequences capture, pre-processing, hand regions detection, feature extraction and gesture classification. The methods of feature extraction are Hidden Markov Model (HMM), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). The main ideas of each technique are described in detail.

Dynamic Hand Gesture Recognition Using Kinematic Features Based on Dynamic Time Warping

2012 ◽  
Vol 235 ◽  
pp. 68-73
Author(s):  
Hai Bo Pang ◽  
You Dong Ding
Keyword(s):  
Gesture Recognition ◽  
Dynamic Time Warping ◽  
Hand Gesture Recognition ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Direction Vector ◽  
Time Warping ◽  
Motion Direction ◽  
Hand Motion ◽  
Dynamic Time

Hand gesture provides an attractive alternative to cumbersome interface devices for human computer interface. Many hand gesture recognition methods using visual analysis have been proposed. In our research, we exploit multiple cues including divergence features, vorticity features and hand motion direction vector. Divergence and vorticity are derived from the optical flow for hand gesture recognition in videos. Then these features are computed by principal component analysis method. The hand tracking algorithm finds the hand centroids for every frame, computes hand motion direction vector. At last, we introduced dynamic time warping method to verify the robustness of our features. Those experimental results demonstrate that the proposed approach yields a satisfactory recognition rate.

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