Abstract
This review considers previous research, regarding the background and applications of 3D face-tracking 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.
Highlights
The use of 3D face tracking involves the application of both physiological and, to a higher degree, behavioural traits of an individual, which has attracted the attention of research organisations [1]
Some challenges that the technique faces include image acquisition and imaging conditions. 3D face tracking is associated with the circumstances of lighting, variations associated with large poses, and ageing occlusions
FACE TRACKING METHODS Face tracking methods can be grouped on the basis of the nature of the utilised technique; the methods discussed include KLT feature trackers [4], particle filter/Kalman filter method [5], probability hypothesis density (PHD) filters for multiple target tracking [6], tracing learning detection (TLD) [7] method, and Shift/CamShift [8] methods
Summary
The use of 3D face tracking involves the application of both physiological and, to a higher degree, behavioural traits of an individual, which has attracted the attention of research organisations [1]. The problem of lighting has proven so prominent that it has hindered the perfect use and building of a robust system of camera control This is the main challenge system designers have faced; for the generation of simple images of the same person, ignoring facial dissemination due to variations in light capacity requires the application of a poserobust albedo estimation. Even albedo estimation has a serious limitation Another problem associated with the use of automated 3D face tracking is the pose or viewpoint, whereby the images under scrutiny may vary as the result of the camera’s position of inclination. This factor may force some parts of the face to be partially or wholly occluded from the image representation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
