Selected Single Face Tracking in Technically Challenging Different Background Video Sequences Using Combined Features

Gowramma²

2018

IJITCS

Due to the lack of particular algorithms for automatic detection and tracking of person face(s), we have developed a new algorithm to achieve detection and single/multiple face tracking in different background video sequence. To detect faces, skin sections are segmented from the frame by means of YCbCr color model; and facial features are used to agree whether these sections contain person face or not. This procedure is challenging, because face color is unique and some objects may have similar color. Further, color and Eigen features are extracted from detected faces. Based on the points detected in facial region, point tracker tracks the user specified number of faces throughout the video sequence. The developed algorithm was tested on challenging dataset videos; and measured for performance using standard metrics. Test results obtained ensure the efficiency of proposed algorithm at the end.

Section: Related Workmentioning

confidence: 99%

Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence

Gowramma²

2018

IJITCS

“…KLT algorithm is a core for the feature tracking algorithms as many of the feature trackers are based on the point values. Ranganatha et al [23][24][25][26][27] have proposed several algorithms that are based on KLT. These algorithms are robust enough to track both single and multiple face in different background video sequence.…”

Section: Recent Developmentsmentioning

confidence: 99%

“…The algorithms addressed many questions which were left unanswered previously such as, 1) Tracking multiple face [24,25], 2) Selective face tracking [26] i.e. user specified number of face(s) tracking, 3) Selected single face tracking [27], 4) Occlusion (e.g. spectacle, beard, headscarf etc.)…”

Section: Recent Developmentsmentioning

confidence: 99%

“…spectacle, beard, headscarf etc.) [25,26], 5) Low resolution [23][24][25][26][27], 6) Face skin colored regions [26], 7) Abrupt motions [23][24][25][26][27], 8) Variations in pose, expression and illumination [23][24][25][26][27] etc. to name a few.…”

Section: Recent Developmentsmentioning

confidence: 99%

“…Some of the primary features used by these algorithms are centroid [23], mean [24,25] and color [26]. These algorithms have also adopted the secondary features such as SURF [21], Haar [21,23], BRISK [24], FAST [25,27], and HOG [27] etc.…”

Section: Recent Developmentsmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey of Algorithms for Face Tracking in different Background Video Sequence

P²

2018

IJCA

Video processing is an interesting research zone in image processing. Face tracking is part of video processing, where the face regions need to be detected and tracked. In this paper, we present a survey of some of the familiar algorithms that are used for tracking the face(s) in different background challenging video sequences. Mean-Shift is an important algorithm that is based on the displacement of points. Improvisation of Mean-Shift lead to the development of CAMSHIFT; the latter is one of the robust chromatic tracking approach developed till date. KLT is an efficient point tracking algorithm. This paper also includes a survey of different motion estimation algorithms, which are classified as either pixel based or feature based. At the end, recent developments help in knowing the relevant works that are being carried out now a days.

Eigen and HOG Features based Algorithm for Human Face Tracking in Different Background Challenging Video Sequences

Gowramma²

2022

IJIGSP

We are proposing a unique novel algorithm for tracking human face(s) in different background video sequences. In the beginning, Eigen features and corner points are extracted from the detected face(s). HOG (Histograms of Oriented Gradients) features are isolated from corner points. Eigen and HOG features are combined together. Using these combined features, point tracker keeps track of the face(s) in the frames of the video sequence. Proposed algorithm is being tested on challenging datasets video sequences with technical challenges such as partial occlusion (e.g. moustache, beard, spectacles, helmet, headscarf etc.), changes in expression, variations in illumination and pose; and measured for performance using standard metrics such as accuracy, precision, recall and specificity. Experimental results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.