Abstract
Given the popularity of hand gesture sign language, automatic interpretation of different gestures has received ever increasing interests. However, owing to the complex of background and similarity between different gestures, a more robust method is needed for effective gesture recognition. In this paper, given the robustness of depth image, a depth image based segmentation is designed to extract the gesture region, while Convolutional Neural Network (CNN) and support vector machine (SVM) are trained respectively for feature extraction and gesture recognition. Experiments on America Sign Language dataset demonstrate that our method is promising and more efficient than some existing methods like HSF + RDF, SIFT + PLS, MPC and classical CNN.
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Acknowledgement
This work is supported by National Natural Science Foundation of China under grants 61601274, 61501286 and 61501287, Natural Science Basic Research Plan in Shaanxi Province of China under grants 2015JQ6208 and 2016NY-176, Coordinator Innovative Engineering Project of Shaanxi Province under grant 2015KTTSGY04-06 and the Fundamental Research Funds for the Central Universities under grants GK201503064 and GK201603083.
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Ma, M., Chen, Z., Wu, J. (2016). A Recognition Method of Hand Gesture with CNN-SVM Model. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_33
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DOI: https://doi.org/10.1007/978-981-10-3611-8_33
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