The Hand Recognition Using SURF and Neural Network

• Main Authors: Yu-Yu Chieh, 尤郁傑
• Other Authors: Shih-Hsiung Twu
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/81430951793655396849

碩士 === 中原大學 === 電機工程研究所 === 103 === In this thesis, we propose a method to recognize the hand gesture on a wide range of distance using Kinect and control the mouse cursor on computer monitor at real-time. We use Speeded-up Robust Feature to extract feature vectors from hand image. Then we recognize these features by neural network. It can be effective in hand recognition at real-time. The system consists of three steps. The first step is hand image capturing and processing. We use the original skeleton tracking technology from Kinect to capture the hand’s depth image and we make the depth image be a binary image. We also compare the binary image with the image that consists of color image and binary image. The second step is feature extracting. We use the modified Speeded-up Robust Feature (SURF) to extract image’s feature vectors. The modified SURF simplifies the interest region detection that selected five fix regions. The final step is hand recognition. We recognize hand features by neural network. In order to get good efficiency at hand recognition between 1 and 3 meters, we need to use sufficient training data to improve the weights in the neural network. The contributions of our research are as follows: (1) Our hand recognition system has the robustness in different illuminations. (2) Our hand recognition system provides the wide range of recognition.