Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation
Md. Zahirul Islam, Mohammad Shahadat Hossain, Raihan Ul Islam, Karl Andersson · 2019
Computer is a part and parcel in our day to day life and used in various fields. The interaction of human and computer is accomplished by conventional input devices like mouse, keyboard etc. Hand gestures can be a useful medium of human-computer interaction and can make the interaction easier. Gestures vary in orientation and shape from person to person. So, non-linearity exists in this problem. Recent research has proved the supremacy of Convolutional Neural Network (CNN) for image representation and classification. Since, CNN can learn complex and non-linear relationships among images, in this paper, a static hand gesture recognition method deploying CNN was proposed. Data augmentation like re-scaling, zooming, shearing, rotation, width and height shifting was applied to the dataset. The model was trained on 8000 images and tested on 1600 images which were divided into 10 classes. The model with augmented data achieved accuracy 97.12% which is nearly 4% higher than the model without augmentation (92.87%).