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To accomplish this objective, we first applied skin color detection and noise processing to remove unnecessary background information from the captured image, before applying background subtraction for detection of the ROI. Then, to prevent background objects or noise from influencing the ROI, we utilized the kernelized correlation filters (KCF) algorithm to implement tracking of the detected ROI. Next, the size of the ROI image was resized to 100\u00d7120 and input into a deep convolutional neural network (CNN) to enable the identification of various hand gestures. In the present study, two deep CNN architectures modified from the AlexNet CNN and VGGNet CNN, respectively, were developed by substantially reducing the number of network parameters used and appropriately adjusting internal network configuration settings. Then, the tracking and recognition process described above was continuously repeated to achieve immediate effect, with the execution of the system continuing until the hand is removed from the camera range. The results indicated excellent performance by both of the proposed deep CNN architectures. In particular, the modified version of the VGGNet CNN achieved better performance with a recognition rate of 99.90% for the utilized training data set and a recognition rate of 95.61% for the utilized test data set, which indicate the good feasibility of the system for practical applications.<\/jats:p>","DOI":"10.3233\/jifs-200385","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T13:17:12Z","timestamp":1591103832000},"page":"4405-4418","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":20,"title":["Hand gesture recognition via image processing techniques and deep CNN"],"prefix":"10.1177","volume":"39","author":[{"given":"Yao-Liang","family":"Chung","sequence":"first","affiliation":[{"name":"Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung City, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hung-Yuan","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Central University, Taoyuan City, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Feng","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Central University, Taoyuan City, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,5,30]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Microsoft: Kinect for Windows. 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