Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9iS1/109672
Year: 2016, Volume: 9, Issue: Special Issue 1, Pages: 1-10
Original Article
Dipti Mishra1*, Mohamed Hashim Minver2 , Bhagwan Das3 , Nisha Pandey4 and Vishal Jain5
1 ECE Department, Pranveer Singh Institute of Technology, Kanpur - 209305, Uttar Pradesh, India; [email protected]
2 Addalaichenai National College, Srilanka; [email protected]
3 University Tun Hussein Onn Malaysia, Malaysi; [email protected]
4 Gyancity Research Lab, India; [email protected]
5 Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM) New Delhi - 110063, Delhi, India; [email protected]
*Author for correspondence
Dipti Mishra
ECE Department
Email: [email protected]
In this paper, a comprehensive scheme is proposed for unconstrained joint face detection and recognition in video sequences for surveillance systems. Unlike conventional video based face recognition techniques, emphasis is laid on the acquisition of a pose constrained training video database followed by the extraction of well aligned face images from the training videos. We have proposed a new Indian Faces Video Database (IFVD) to demonstrate the performance of the proposed approach especially in the challenging environment of varying skin color and texture of faces from the Indian subcontinent. Our approach produces successful face tracking results on over 86% of all videos. The good tracking performance induces high recognition rates: 85.86 on Honda/UCSD and over 77.49 % on IFVD. The proposed technique is robust and aims to develop a unified framework to address the challenges of varying head orientation, pose and illumination level in a highly integrated fashion so as to benefit from the interdependence between the high fidelity face detection and the subsequent recognition phases.
Keywords: Adaboost, Classification, Face Detection, Face Recoginition, Kalman Tracking, Manifold Learning, SVM
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