Orthogonal Quadratic Discriminant Functions for Face Recognition

Authors:
Suicheng Gu;Ying Tan;Xingui He
Affiliations:
Key Laboratory of Machine Perception(MOE), Peking University/ Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, P.R. China 100 ...;Key Laboratory of Machine Perception(MOE), Peking University/ Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, P.R. China 100 ...;Key Laboratory of Machine Perception(MOE), Peking University/ Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, P.R. China 100 ...
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

Citing 10
Cited 0

Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Regularized discriminant analysis for the small sample size problem in face recognition

Pattern Recognition Letters
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Kernel quadratic discriminant analysis for small sample size problem

Pattern Recognition
Building compact MQDF classifier for large character set recognition by subspace distribution sharing

Pattern Recognition
Discriminative learning quadratic discriminant function for handwriting recognition

IEEE Transactions on Neural Networks
High-speed face recognition based on discrete cosine transform and RBF neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Small sample size (SSS) problem is usually a limit to the robustness of learning methods in face recognition. Especially in the quadratic discriminant functions (QDF), too many parameters need to be estimated and covariance matrix of a class is usually singular. In order to overcome the SSS problems, we proposed a novel approach called orthogonal quadratic discriminant functions (OQDF). The OQDF assumes probability distribution functions of each two classes of face images have a uniform shape. Then, three OQDF models are developed. The Laplacian smoothing transform (LST) and Fisher's linear discriminant (FLD) are employed to preprocess the face images for the OQDF classifier. Finally, we evaluate our proposed algorithms on two face databases, ORL and Yale.