Transfer Learning for Improved Hand Gesture Recognition with Neural Networks

Hand recognition is crucial in various applications, such as gesture recognition, augmented and virtual reality. Hand gestures have the potential to facilitate human-computer interaction and enhance its convenience. The characteristics of gestures, such as their orientation and shape, differ among individuals, resulting in non-linearities in the problem. Recent studies have demonstrated the superior performance of Convolutional Neural Networks (CNNs) in accurately representing and classifying images, further emphasizing their effectiveness in this context. This paper proposes a novel deep learning model that is augmented using a transfer learning technique. Further, we trained our model using hand gesture recognition datasets of near-infrared images captured by the Leap Motion sensor. The proposed model is composed of ten classes that represent personalized gestures. The model with augmented data achieved an accuracy 99.12% which is nearly 2% higher than the model without augmentation (97.87%). The results demonstrate the robustness and efficiency of our model compared with the state-of-the-art models.