A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast fourier transforms: a tutorial review and a state of the art
Signal Processing
Algebraic feature extraction of image for recognition
Pattern Recognition
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose estimation of SAR imagery using the two dimensional continuous wavelet transform
Pattern Recognition Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance measures for PCA-based face recognition
Pattern Recognition Letters
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
An Efficient Two-Dimensional FFT Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bidirectional PCA with assembled matrix distance metric for image recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A note on two-dimensional linear discriminant analysis
Pattern Recognition Letters
2DPCA-based techniques in DCT domain for face recognition
International Journal of Intelligent Systems Technologies and Applications
Classification of three-way data by the dissimilarity representation
Signal Processing
Probabilistic learning of similarity measures for tensor PCA
Pattern Recognition Letters
| Hi-index | 0.10 |
Two-dimensional principal component analysis (2DPCA) is based on the 2D images rather than 1D vectorized images like PCA, which is a classical feature extraction technique in face recognition. Many 2DPCA-based face recognition approaches pay a lot of attention to the feature extraction, but fail to pay necessary attention to the classification measures. The typical classification measure used in 2DPCA-based face recognition is the sum of the Euclidean distance between two feature vectors in a feature matrix, called distance measure (DM). However, this measure is not compatible with the high-dimensional geometry theory. So a new classification measure compatible with high-dimensional geometry theory and based on matrix volume is developed for 2DPCA-based face recognition. To assess the performance of 2DPCA with the volume measure (VM), experiments were performed on two famous face databases, i.e. Yale and FERET, and the experimental results indicate that the proposed 2DPCA+VM can outperform the typical 2DPCA+DM and PCA in face recognition.