The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Face recognition using LDA mixture model
Pattern Recognition Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A classifier for Bangla handwritten numeral recognition
Expert Systems with Applications: An International Journal
An Algorithm for License Plate Recognition Applied to Intelligent Transportation System
IEEE Transactions on Intelligent Transportation Systems
An efficient approach for face recognition based on common eigenvalues
Pattern Recognition
Hi-index | 12.05 |
An improved discriminative common vectors and support vector machine based face recognition approach is proposed in this paper. The discriminative common vectors (DCV) algorithm is a recently addressed discriminant method, which shows better face recognition effects than some commonly used linear discriminant algorithms. The DCV is based on a variation of Fisher's Linear Discriminant Analysis for the small sample size case. However, for multiclass problem, the Fisher criterion is clearly suboptimal. We design an improved discriminative common vector by adjustment for the Fisher criterion that can estimate the within-class and between-class scatter matrices more accurately for classification purposes. Then we employ support vector machine as the classifier due to its higher classification and higher generalization. Testing on two public large face database: ORL and AR database, the experimental results demonstrate that the proposed method is an effective face recognition approach, which outperforms several representative recognition methods.