Optimized Face Tracking
Abstract During the past few years face detection and tracking from video has received maximum importance because of commercial and enforcement application of varied an extreme range. It is also most challenging task in video, where the variation of illuminations, noise, locations of human face and pose can differ from one frame to another. For face detection and tracking from video database is being presented a unique technique in this paper. In this study, primary goal is to recognize location of faces from video. Moreover, finding face motion leads to be a part of face recognition system. Firstly, face edges are detected using Robert edge detector followed by a set of arithmetic operations between an initial frame and the nearest ones. Thereafter, non-desired edges and noise are removed by Gaussian filtering technique. Optimized Face Tracking A logical operation is then performed between the previous two output frames and noiseless face contour frame for detecting edges
corresponding to face video. Finally, four corner points i.e. topleft, top-right, bottom-left, bottom-right are computed to draw rectangle around the face and detect face contour of each frame. To track human face from video, scalar and vector distance between four corner points of two consecutive frames are calculated. Displacement of corner points means position and location of face changes in the next frame. On Honda/UCSD video database the proposed method has been tested and it has been found through experimental results that
it can detect and track from video efficiently human face.
The problem of online face tracking from unconstrained videos is still unresolved. Challenges range from coping with severe online appearance variations to coping with occlusion. We propose RFTD (Robust Face Tracking-by-Detection), a system which combines tracking and detection into a single framework to robustly track a face from unconstrained videos. RFTD is based on the idea that adaptive and stable algorithmic components can complement each other in the task of online tracking. An online Structured Output SVM
(SO-SVM) is combined with an offline trained face detector to break the self-learning loop typical in tracking. In turn, the face detector is supervised by a Deformable Part Model (DPM) landmark detector to asses the reliability of the face detection output. Extensive evaluation shows that RFTD delivers consistently good tracking performances across different scenarios, i.e., high mean success rate and lowest standard
deviation across benchmark videos.