scholarly journals Lower Order Krawtchouk Moment-Based Feature-Set for Hand Gesture Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Bineet Kaur ◽  
Garima Joshi
Keyword(s):  
Gesture Recognition ◽  
Lower Order ◽  
Shape Recognition ◽  
Region Of Interest ◽  
Hand Gesture ◽  
Shape Features ◽  
Krawtchouk Polynomials ◽  
Indian Sign Language ◽  
Recognition Capability ◽  
Original Feature

The capability of lower order Krawtchouk moment-based shape features has been analyzed. The behaviour of 1D and 2D Krawtchouk polynomials at lower orders is observed by varying Region of Interest (ROI). The paper measures the effectiveness of shape recognition capability of 2D Krawtchouk features at lower orders on the basis of Jochen-Triesch’s database and hand gesture database of 10 Indian Sign Language (ISL) alphabets. Comparison of original and reduced feature-set is also done. Experimental results demonstrate that the reduced feature dimensionality gives competent accuracy as compared to the original feature-set for all the proposed classifiers. Thus, the Krawtchouk moment-based features prove to be effective in terms of shape recognition capability at lower orders.

Extraction of Hand Gesture Features for Indian Sign languages using Combined DWT-DCT and Local Binary Pattern

2018 ◽  
Vol 7 (2.24) ◽  
pp. 316
Author(s):  
Muthukumar. K ◽  
Poorani S ◽  
Gobhinath S
Keyword(s):  
Skin Color ◽  
Local Binary Pattern ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Hand Gesture ◽  
Discrete Wavelet ◽  
Sign Languages ◽  
Sobel Operator ◽  
Language Data ◽  
Indian Sign Language

The Hand Gesture system is based on two modes, viz, Enrollment mode and Recognition mode. In the enrollment mode, the Hand features are acquired from the camera and stored in a database along with the Sign languages. In the recognition mode, the hand features are re-acquired from the camera and compared against the stored Indian sign language data to determine the exact signs. In the pre-processing stage, two segmentation processes are proposed to extract the region of interest (ROI) of hand gesture. The first skin-color segmentation is used to extract the hand image from the background. The second region of interest of the hand gesture is segmented by using the valley detection algorithm. The Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are applied for the purpose of extracting the features. Further, the Sobel Operator and Local Binary Pattern (LBP) are used for increasing the number of features. The mean and standard deviation of DWT, DCT and LBP are computed.   

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