Poster Papers

Advances in Pattern Recognition

Volume 1451 of the series Lecture Notes in Computer Science pp 1013-1020


Subject-based modular eigenspace scheme for face recognition

  • Bai-ling ZhangAffiliated withDepartment of Electrical and Computer Engineering, University of Newcastle
  • , Min-yue FuAffiliated withDepartment of Electrical and Computer Engineering, University of Newcastle
  • , Hong YangAffiliated withDepartment of Electrical Engineering, University of Sydney

* Final gross prices may vary according to local VAT.

Get Access


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.