Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
On the computational aspects of Zernike moments
Image and Vision Computing
EURASIP Journal on Applied Signal Processing
Face Recognition Using Improved Fast PCA Algorithm
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 1 - Volume 01
Human Face Recognition Using Different Moment Invariants: A Comparative Study
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 3 - Volume 03
Two-dimensional subspace classifiers for face recognition
Neurocomputing
A novel Bayesian logistic discriminant model: An application to face recognition
Pattern Recognition
Face recognition using a fuzzy fisherface classifier
Pattern Recognition
Higher order orthogonal moments for invariant facial expression recognition
Digital Signal Processing
Face recognition using Zernike and complex Zernike moment features
Pattern Recognition and Image Analysis
Hi-index | 0.00 |
Usually magnitude coefficients of some selected orders of ZMs and PZMs have been used as invariant image features. The careful selection of the set of features, with higher discrimination competence, may increase the recognition performance. In this paper, the authors have used a statistical method to estimate the discrimination strength of all the extracted coefficients of ZMs and PZMs whereas for classification, only the coefficients with estimated higher discrimination strength are used in the feature vector. The performance of these selected Discriminative ZMs DZMs and Discriminative PZMs DPZMs features have been compared to that of their corresponding conventional approaches on YALE, ORL and FERET databases against illumination, expression, scale and pose variations. An extension to these DZMs and DPZMs have been proposed by combining them with PCA and FLD. It has been observed from the exhaustive experimentation that the recognition rate is improved by 2-6%, at reduced dimensions and with less computational complexity, than that of using the successive ZMs and PZMs features.