Facial feature tracking method using a hybrid model of the Kalman filter and the sliding innovation filter

Abstract

The purpose of this paper is to aid in detecting synthesized video (specifically created through the use of DeepFake) by exploring facial-feature tracking methods. Analyzing individual facial features, should allow for more successful detection of DeepFake videos according to H. Nguyen et al.’s research [22] and A. A. Maksutov’s list of commonly use techniques to identify fabricated media [17]. To detect these facial features in images, Computer Vision techniques such as YOLOv3 [24] can be used. Once detected, object-tracking methods should be explored. This paper will compare the accuracy of three existing object-tracking methods: the minimum-distance approach, the Kalman Filter (KF) method, and the Sliding Innovation Filter (SIF) method. Following this comparison, the paper proposes a novel hybrid object-tracking approach, in which the benefits of the KF method and SIF method are combined to provide a time-gap tolerant object-tracking method. Each of the models are tested on their ability to track multiple objects that follow different trajectories and compared against one another to identify the most effective manner of tracking.