Document Type
Article
Publication Date
2011
Publication Source
Complex Adaptive Systems
Abstract
We propose a real time system for person detection, recognition and tracking using frontal and profile faces. The system integrates face detection, face recognition and tracking techniques. The face detection algorithm uses both frontal face and profile face detectors by extracting the 'Haar' features and uses them in a cascade of boosted classifiers. The pose is determined from the face detection algorithm which uses a combination of profile and frontal face cascades and, depending on the pose, the face is compared with a particular set of faces having the same range for classification. The detected faces are recognized by projecting them onto the Eigenspace obtained from the training phase using modular weighted PCA and then, are tracked using the Kalman filter multiple face tracker. In this proposed system, the pose range is divided into three bins onto which the faces are sorted and each bin is trained separately to have its own Eigenspace. This system has the advantage of recognizing and tracking an individual with minimum false positives due to pose variations.
ISBN/ISSN
1877-0509
Document Version
Published Version
Publisher
Procedia Computer Science
Volume
6
Peer Reviewed
yes
eCommons Citation
Nair, Binu Muraleedharan; Foytik, Jacob; Tompkins, Richard; Diskin, Yakov; Aspiras, Theus; and Asari, Vijayan K., "Multi-Pose Face Recognition and Tracking System" (2011). Electrical and Computer Engineering Faculty Publications. 471.
https://ecommons.udayton.edu/ece_fac_pub/471
Included in
Computer Engineering Commons, Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1016/j.procs.2011.08.070