YOLO-Based Deep-Learning Gaze Estimation Technology by Combining Geometric Feature and Appearance Based Technologies for Smart Advertising Displays

In this study, a YOLO-based deep-learning gaze estimation technology is developed for the application of non-contact smart advertising displays. By integrating the appearance and geometric-features technologies, the output coordinates of facial features inferred by YOLOv3-tiny based models can provide the training data for gaze estimation without the calibration process. In experiments, the input size of YOLOv3-tiny based models is arranged by 608x608 pixels, and the used models have good location performance to detect the facial directions and two facial features. By the YOLOv3-tiny based model with the cross-person test, the proposed method performs the averaged gaze estimation accuracies of nine, six, and four-block modes are 66.38%, 80.87%, 88.34%, respectively with no calibration process.