AN EFFICIENT LIGHT WEIGHT CONVOLUTIONAL NEURAL NETWORK FOR HAND GESTURE RECOGNITION

Abstract

Following the breakout of COVID-19, there has been a significant increase in demand for gesture sensing applications, which allow users to manage gadgets with simple hand gestures rather than physically touching them. In comparison to expressions, actions, and other interaction techniques, gestures are more intuitive, straightforward, and natural. For Smart AI assistants, a cognitive vision system is essential for enabling seamless interaction with humans. Among the various features in the vision system, the ability to detect and recognize different hand gestures provides significant value addition. Systems employing hand gesture recognition technology are capable of distinguishing specific gestures such as victory sign, thumbs up, wave, peace sign, rock sign, number counting, etc. Real-world systems designed for human-computer interaction struggle to recognise and categorize hand gestures because 1) People perform gestures in a wide variety of ways, based on their cultural difference, 2) Variability of input lighting, distance limits, 3) Requirements of larger datasets etc. The wearable glove-based sensor technique and the camera vision-based sensor approach are the two main strategies for hand gesture recognition research. In this work, images captured using camera sensors are used as the input and fed into the proposed shallow convolutional neural network for classification and prediction of hand gestures. The proposed low weight convolutional neural network achieved faster training results by using fewer parameters and training epochs. Three well-known pre-trained models, including VGG16, ResNet50, and Mobilenet, are also taken into consideration for comparison. These models are applied on Fingers number count and LeapGestRecogn dataset and evaluation measures such as F1 score, recall, accuracy, and precision are computed to analyze the performance of the models.The experimental results indicate that the proposed model has achieved a recognition rate of 99.9% and 99.79% on LeapGestRecogn and Fingers number count dataset respectively. Furthermore, the proposed model outperformed the state-of-the-art methods with better accuracy