Research on the Hand Gesture Recognition Based on Deep Learning

With the rapid development of computer vision, the demand for interaction between human and machine is becoming more and more extensive. Since hand gestures are able to express enriched information, the hand gesture recognition is widely used in robot control, intelligent furniture and other aspects...

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Bibliographic Details
Published in2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE) pp. 1 - 4
Main Authors Sun, Jing-Hao, Ji, Ting-Ting, Zhang, Shu-Bin, Yang, Jia-Kui, Ji, Guang-Rong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
Gaussian mixture model
Gesture recognition
hand gesture recognition
hand gesture segmentation
hand gesture tracking
Image color analysis
Image segmentation
neural network
Real-time systems
Skin
Target tracking
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Summary:With the rapid development of computer vision, the demand for interaction between human and machine is becoming more and more extensive. Since hand gestures are able to express enriched information, the hand gesture recognition is widely used in robot control, intelligent furniture and other aspects. The paper realizes the segmentation of hand gestures by establishing the skin color model and AdaBoost classifier based on haar according to the particularity of skin color for hand gestures, as well as the denaturation of hand gestures with one frame of video being cut for analysis. In this regard, the human hand is segmentd from the complicated background, the real-time hand gesture tracking is also realized by CamShift algorithm. Then, the area of hand gestures which has been detected in real time is recognized by convolutional neural network so as to realize the recognition of 10 common digits. Experiments show 98.3% accuracy.
DOI:10.1109/ISAPE.2018.8634348