Two-dimensional PCA: a new approach to appearance-based face representation and recognition.
In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
Yang
Jian
J
Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong. jyang@unizar.es
Zhang
David
D
Frangi
Alejandro F
AF
Yang
Jing-yu
JY
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005145:Face / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique