Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Low Rank Approximations of Matrices
Machine Learning
Fast Active Appearance Model Search Using Canonical Correlation Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Face recognition based on 2D Fisherface approach
Pattern Recognition
2DCCA: A Novel Method for Small Sample Size Face Recognition
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Fusing gait and face cues for human gender recognition
Neurocomputing
On the equivalence between canonical correlation analysis and orthonormalized partial least squares
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Facial images dimensionality reduction and recognition by means of 2DKLT
Machine Graphics & Vision International Journal
Face matching between near infrared and visible light images
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Biometric verification of subjects using saccade eye movements
International Journal of Biometrics
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This paper presents the implementation of the method of twodimensional Canonical Correlation Analysis (CCA) and two-dimensional Partial Least Squares (PLS) applied to image matching. Both methods are based on representing the image as the sets of its rows and columns and implementation of CCA using these sets (hence we named the methods as CCArc and PLSrc). CCArc and PLSrc feature simple implementation and lesser complexity than other known approaches. In applications to biometrics, CCArc and PLSrc are suitable to solving the problems when dimension of images (dimension of feature space) is greater than the number of images, i.e., Small Sample Size (SSS) problem. This paper demonstrates high efficiency of CCArc and PLSrc for a number of computer experiments, using benchmark image databases.