How Should We RepresentFaces for Automatic Recognition?
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of similarity measures based on histograms of local image feature vectors
Pattern Recognition Letters
Pattern Recognition
A fast and robust method for the identification of face landmarks in profile images
WSEAS Transactions on Computers
Robust identification of face landmarks in profile images
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Face image retrieval system using TFV and combination of subimages
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
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This paper studies how biologically meaningful landmarks extracted from face images can be exploited for face recognition using the bidimensional regression. Incorporating the correlation statistics of landmarks, this paper also proposes a new approach called eigenvalue weighted bidimensional regression. Complex principal component analysis is used for computing eigenvalues and removing correlation among landmarks. We evaluate our approach using two standard face databases: the Purdue AR and the NIST FERET. Experimental results show that the bidimensional regression is an efficient method to exploit geometry information of face images.