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An efficient gesture based humanoid learning using wavelet descriptor and MFCC techniques
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DOI：10.1007/s13042-016-0512-4.png)
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Abstract
En 中文
Recognizing any gesture, pre-processing and feature extraction are the two major issues which we have solved by proposing a novel concept of Indian Sign Language (ISL) gesture recognition in which a combination of wavelet descriptor (WD) and Mel Sec Frequency Cepstral Coefficients (MFCC) feature extraction technique have been used. This combination is very effective against noise reduction and extraction of invariant features. Here we used WD for reducing dimensionality of the data and moment invariant point extraction of hand gestures. After that MFCC is used for finding the spectral envelope of an image frame. This spectral envelope quality is useful for recognizing hand gestures in complex environment by eliminating darkness present in each gesture. These feature vectors are then used for classifying a probe gestures using support vector machine (SVM) and K nearest neighbour classifiers. Performance of our proposed methodology has been tested on in house ISL datasets as well as on Sheffield Kinect gesture dataset. From experimental results we observed that WD with MFCC method provides high recognition rate as compare to other existing techniques [MFCC, orientation histogram (OH)]. Subsequently, ISL gestures have been transferred to a Humanoid HOAP-2 (humanoid open architecture platform) robot in Webots simulation platform. Then these gestures are imitated by HOAP-2 robot exactly in a same manner.
Keywords:
Computer vision
DWT
Indian sign language
MFCC
SVM
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