Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
IEEE Transactions on Computers
Journal of Cognitive Neuroscience
Identity Management in Face Recognition Systems
Biometrics and Identity Management
The kernel orthogonal mutual subspace method and its application to 3D object recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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This paper describes a new method for face recognition under drastic changes of the imaging processes through which the facial images are acquired. Unlike the conventional methods that use only the face features, the present method exploits the statistical information of the variations between the face image sets being compared, in addition to the features of the faces themselves. To incorporate both of the face and perturbation features for recognition, we develop a technique called weak orihogonalization of the two subspaces that transforms the given two overlapped subspaces so that the volume of the intersection of the resulting two subspaces is minimized. Matching operations are performed in the transformed face space that has thus been weakly orihogonalized against perturbation space. Experimental results on real pictures of the frontal faces from drivers' licenses show that the new algorithm improves the recognition performance over the conventional methods. We also demonstrate the effectiveness of our method on image sets with changes in viewing geometry.