Hand Gesture Recognition Using Capabilities of Capsule Network and Data Augmentation

Gesture Recognition is a technology that makes devices such as a computer capable of recognizing and responding to different gestures produced by the human body. Nonetheless, several factors such as differences in illumination intensity, the complexity of hand gesture models, and other factors can affect the performance of recognition and classification algorithms. Some advances in deep learning such as CapsNets have been suggested to improve the performance of image recognition systems in this particular field. CapsNets emerged to solve part of the limitations of CNN. For this reason, in this work, CapsNets is proposed to solve the American Sign Language problem very effectively. The obtained results showed that the proposed model with a simple data augmentation process produces an test accuracy of 99.08%.