Document Type
Article
Publication Date
2011
Publication Source
Complex Adaptive Systems
Abstract
Real-time tracking and recognizing multiple faces in complex environments has the ability to provide efficient security automation to large areas. Previous research has shown that Kalman filter techniques paired with the traditional face detection methods can be used to track one or more faces in a viewing region, but prove unreliable under variant conditions due to the inability to reliably distinguish between multiple trackers. A real-time face tracking and recognition system is presented that is capable of processing multiple faces simultaneously. The proposed system utilizes the Kalman filter for tracking and uses a low-level recognition system to properly distinguish between the many trackers. This low-level system is created using a face database of twenty unrelated people trained using Modular Principal Component Analysis (MPCA) and classification is performed using a feature correlation metric. After tracking the faces, they are then analyzed by a high-level face recognition subspace which is created using a large database of people and processed using Adaptive MPCA. The overall system is shown to provide reliable tracking of more than one person and to allow a more accurate recognition rate due to the ability to create a time-average of the recognized faces.
ISBN/ISSN
1877-0509
Document Version
Published Version
Publisher
Elsevier Science BV
Volume
6
Peer Reviewed
yes
eCommons Citation
Foytik, Jacob; Sankaran, Praveen; and Asari, Vijayan, "Tracking and Recognizing Multiple Faces using Kalman Filter and Modularpca" (2011). Computer Science Faculty Publications. 185.
https://ecommons.udayton.edu/cps_fac_pub/185
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.2011.08.047