Hand gesture recognition using an adapted convolutional neural network with data augmentation

Hand gestures provide a natural way for humans to interact with computers to perform a variety of different applications. However, factors such as the complexity of hand gesture structures, differences in hand size, hand posture, and environmental illumination can influence the performance of hand gesture recognition algorithms. Recent advances in Deep Learning have significantly advanced the performance of image recognition systems. In particular, the Deep Convolutional Neural Network has demonstrated superior performance in image representation and classification, compared to conventional machine learning approaches. This paper proposes an Adapted Deep Convolutional Neural Network (ADCNN) suitable for hand gesture recognition tasks. Data augmentation is initially applied which shifts images both horizontally and vertically to an extent of 20% of the original dimensions randomly, in order to numerically increase the size of the dataset and to add the robustness needed for a deep learning approach. These images are input into the proposed ADCNN model which is empowered by the presence of network initialization (ReLU and Softmax) and L2 Regularization to eliminate the problem of data overfitting. With these modifications, the experimental results using the ADCNN model demonstrate that it is an effective method of increasing the performance of CNN for hand gesture recognition. The model was trained and tested using 3750 static hand gesture images, which incorporate variations in features such as scale, rotation, translation, illumination and noise. The proposed ADCNN was compared to a baseline Convolutional Neural Network and the results show that the proposed ADCNN achieved a classification recognition accuracy of 99.73%, and a 4% improvement over the baseline Convolutional Neural Network model (95.73%).