Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast features for face authentication under illumination direction changes
Pattern Recognition Letters
Pattern Recognition Letters
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Improved-LDA based face recognition using both facial global and local information
Pattern Recognition Letters
On transforming statistical models for non-frontal face verification
Pattern Recognition
Multiresolution based Kernel Fisher Discriminant Model for Face Recognition
ITNG '07 Proceedings of the International Conference on Information Technology
Journal of Cognitive Neuroscience
Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms
IEEE Transactions on Image Processing
A robust wavelet based feature extraction method for face recognition
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Entropy controlled Laplacian regularization for least square regression
Signal Processing
EURASIP Journal on Advances in Signal Processing - Special issue on quantization of VLSI digital signal processing systems
Discriminative information preservation for face recognition
Neurocomputing
Hi-index | 0.08 |
This paper presents a pattern recognition framework for face recognition based on the combination of Radon and discrete cosine transforms (DCT). The property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. Data compaction property of DCT yields lower-dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. Further, DCT applied on Radon projections provides frequency features. The technique is invariant to in-plane rotation (tilt) and robust to zero mean white noise. The proposed algorithm is evaluated using FERET and ORL databases. The experimental results show the superiority of the proposed method compared to some of the existing algorithms.