Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 3; p. 706
• Main Authors: Sahoo, Jaya Prakash, Prakash, Allam Jaya, Pławiak, Paweł, Samantray, Saunak
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.01.2022; MDPI
• Subjects: Accuracy; Analysis; ASL; Classification; Computer Systems; Datasets; Deep learning; fine-tunning; Gestures; Hand; hand gesture recognition; Humans; Interfaces; Literature reviews; Neural networks; Neural Networks, Computer; Performance evaluation; pre-trained CNN; real-time gesture recognition; Recognition, Psychology; score fusion; Sensors; Sign Language; Support vector machines; score fusion; real-time gesture recognition; ASL; fine-tunning; pre-trained CNN; hand gesture recognition
• DOI: 10.3390/s22030706
• ISSN: 1424-8220

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.