Research on the Hand Gesture Recognition Based on Deep Learning

• Published in: 2018 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
• Language: English
• 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
• DOI: 10.1109/ISAPE.2018.8634348

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.