Depth-based Hand Gesture Recognition Using Hand Movements And Defects

• Main Authors: Wei-Lun Chen, 陳帷綸
• Other Authors: none
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/39155254521751013827

碩士 === 國立臺灣科技大學 === 電子工程系 === 103 === The hand gesture recognition has a long history within the computer vision community and is one natural and intuitional way to communicate with human and machine. Since low-cost depth cameras have been launched, depth cameras become more and more affordable in consumer electronics. In this thesis, we proposed a dynamic hand gesture recognition system by using only the depth information. The proposed system can recognize twelve different dynamic hand gestures, including swipes, scales, push, wave, rotates, circle, and drag. First, the background subtraction is used to remove the unnecessary information, and the depth information of main user can be obtained. Furthermore, hand position can be tracked, and the region of hand is extracted as an adaptive square. Once the region of hand is obtained, the hand parameters are obtained by calculating the depth information of hand region. The proposed system can recognize dynamic hand gesture by using the hand parameters. In the experiment, the performance of the proposed system is verified by two different people at 2 different depths, and both right and left hands are verified. The experimental result show that the proposed system can recognize the dynamic hand gestures with an average recognition rate of 92.95%.