{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:59:56Z","timestamp":1770044396831,"version":"3.49.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>The real-time perception of hand gestures in a deprived environment is a demanding machine vision task. The hand recognition operations are more strenuous with different illumination conditions and varying backgrounds. Robust recognition and classification are the vital steps to support effective human-machine interaction (HMI), virtual reality, etc. In this paper, the real-time hand action recognition is performed by using an optimized Deep Residual Network model. It incorporates a RetinaNet model for hand detection and a Depthwise Separable Convolutional (DSC) layer for precise hand gesture recognition. The proposed model overcomes the class imbalance problems encountered by the conventional single-stage hand action recognition algorithms. The integrated DSC layer reduces the computational parameters and enhances the recognition speed. The model utilizes a ResNet-101 CNN architecture as a Feature extractor. The model is trained and evaluated on the MITI-HD dataset and compared with the benchmark datasets (NUSHP-II, Senz-3D). The network achieved a higher Precision and Recall value for an IoU value of 0.5. It is realized that the RetinaNet-DSC model using ResNet-101 backbone network obtained higher Precision (99.21 %for AP0.5, 96.80%for AP0.75) for MITI-HD Dataset. Higher performance metrics are obtained for a value of \u03b3=\u200a2 and \u03b1=\u200a0.25. The SGD with a momentum optimizer outperformed the other optimizers (Adam, RMSprop) for the datasets considered in the studies. The prediction time of the optimized deep residual network is 82\u200ams.<\/jats:p>","DOI":"10.3233\/jifs-210875","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T18:57:35Z","timestamp":1633633055000},"page":"6983-6997","source":"Crossref","is-referenced-by-count":6,"title":["In-situ identification and recognition of multi-hand gestures using optimized deep residual network"],"prefix":"10.1177","volume":"41","author":[{"given":"S.","family":"Rubin Bose","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Madras Institute of Technology Campus, Anna University, Chennai, India"}]},{"given":"V.","family":"Sathiesh Kumar","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Madras Institute of Technology Campus, Anna University, Chennai, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210875_ref1","doi-asserted-by":"publisher","first-page":"022001","DOI":"10.1088\/1742-6596\/1213\/2\/022001","article-title":"Hand Gesture Recognition with Skin Detection and Deep Learning Method","volume":"1213","author":"Huang","year":"2019","journal-title":"IOP: Journal of Physics: Conf. 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