This review considers previous research, regarding the background and applications of 3D facetracking systems with a focus on stereo camera-based systems. Stereo cameras are less expensive than laser ranging systems, and they are widely available on devices such as smart phones. This review aims to spur further development and applications of face tracking in this domain. Many studies on face tracking have used concepts such as the Kanade-Lucas-Tomasi method, particle filters, tracking-learning-detecting, probability hypothesis density, mean shift/cam shift, and others. As imaging constraints are relaxed, facial tracking becomes more challenging. This review presents an exposition of the most common challenges in face tracking, such as occlusion and clutter, pose variations, changes in facial resolution, illumination variations, and facial deformation. Five forms of pose estimation are discussed: appearance template methods, detector arrays, flexible models, geometric methods, and tracking methods. Applications of the listed 3D face tracking systems are also discussed, including face modelling, film editing, access control, security, and surveillance.

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3D facial tracking has become vital to the continued integration of computers, technology, and human society. In recent decades, the integration of technology has increased, and the use of surveillance, conference calls, gaming components, and other similar applications has spurred demand for the ability to recognize the distinctive features of humans. However, in order for these new technologies to function effectively and reach their fullest potential, a great deal of work is still needed. The field of facial mapping and tracking is still in its early developmental stages, necessitating additional research into the best methods of tracking and monitoring specific human faces. To this end, an algorithm has been created that would allow for improvements in this area; however, a video was first required that could be used effectively for the algorithm. Two web cameras running on Raspberry Pi were used to gather the footage necessary for detecting and tracking specific facial features. While certain limitations were identified throughout the process, the algorithm still achieved significant successful tracking results. In spite of this success, further efforts are still needed to effectively explore the proposed algorithm and improve upon these initial results.

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