An Efficient Hand Gesture Recognition System Based on Deep CNN

The goal of this paper is to use a webcam to instantly track the region of interest (ROI), namely, the hand region, in the image range and identify hand gestures for home appliance control (in order to create smart homes) or human-computer interaction fields. Firstly, we use skin color detection and morphology to remove unnecessary background information from the image, and then use background subtraction to detect the ROI. Next, to avoid background influences on objects or noise affecting the ROI, we use the kernelized correlation filters (KCF) algorithm to track the detected ROI. The image size of the ROI is then resized to 100x120 and then entered into the deep convolutional neural network (CNN), in order to identify multiple hand gestures. Two deep CNN architectures are developed in this study that are modified from AlexNet and VGGNet, respectively. Then, the above process of tracking and recognition is repeated to achieve an instant effect, and the system's execution continues until the hand leaves the camera range. Finally, the training data set can reach a recognition rate of 99.90%, and the test data set has a recognition rate of 95.61%, which represents the feasibility of the practical application.