Author : D.Nagarathinam 1
Date of Publication :22nd March 2018
Abstract: In this paper, The detection of human face from images plays a vital role in Computer vision, cognitive science and Forensic Science. The various computational and mathematical models, for classifying face including Scale Invariant Feature transform (SIFT) and Dominant Rotated Local Binary Pattern (DRLBP) have been proposed yields better performance. This is done by pre-processing the face image at first and then extracting the face features using SIFT. Then the detection of human faces is done using Optical flow. The process of combining SIFT and DRLBP perform better rather using separately. The face tracking stage is based on Optical Flow algorithm. Optical Flow is implemented in the proposed framework with two feature extraction methods, Fast Corner Features, and Regular Features.It also relies on a pixel-level feedback scheme that automatically adjusts internal sensitivity to change and update rates. Our approach continuously monitors both local model fidelity and segmentation noise to guide these adjustments, allowing for fast responses to intermittent dynamic background motion. As such, it can be effectively used in complex surveillance scenarios presenting many different challenges simultaneously
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