01530nas a2200169   4500000000100000000000100001008004100002100001500043700001800058700001800076700002100094245008600115856004400201490000700245520109400252022001401346       2022                            d1 aJaya Sahoo1 aAllam Prakash1 aPawel Plawiak1 aSaunak Samantray00aReal-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network.  uhttps://www.mdpi.com/1424-8220/22/3/7060 v223 a<p>Hand gesture recognition is one of the most effective modes of interaction between humans and computers due to being highly flexible and user-friendly. A real-time hand gesture recognition system should aim to develop a user-independent interface with high recognition performance. Nowadays, convolutional neural networks (CNNs) show high recognition rates in image classification problems. Due to the unavailability of large labeled image samples in static hand gesture images, it is a challenging task to train deep CNN networks such as AlexNet, VGG-16 and ResNet from scratch. Therefore, inspired by CNN performance, an end-to-end fine-tuning method of a pre-trained CNN model with score-level fusion technique is proposed here to recognize hand gestures in a dataset with a low number of gesture images. The effectiveness of the proposed technique is evaluated using leave-one-subject-out cross-validation (LOO CV) and regular CV tests on two benchmark datasets. A real-time American sign language (ASL) recognition system is developed and tested using the proposed technique.</p>
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