Abstract
Face recognition is an important research area with many potential applications such as biometric security. Among various techniques, eigenface method by principal component analysis (PCA) of face images has been widely used. In traditional eigenface methods, PCA was used to get the eigenvectors of the covariance matrix of a training set of face images and recognition was achieved by applying a template matching scheme with the vectors obtained by projecting new faces along a small number of eigenfaces. In order to avoid the time consuming step of recomputing eigenfaces when new faces are added, we use a set of modules to generate PCA based face representation for each subjects instead of PCA of entire face images. The localized nature of the representation makes the system easy to maintain and tolerant of local facial characteristic changes. Results indicate that the modular scheme yield accurate recognition on the widely used Olivetti Research Laboratory (ORL) face database.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
