Face recognition with one training image per person
Pattern Recognition Letters
Multimodal decision-level fusion for person authentication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multimodal biometrics using geometry preserving projections
Pattern Recognition
Feature-Level Fusion of Iris and Face for Personal Identification
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Gabor texture in active appearance models
Neurocomputing
Palmprint verification using binary orientation co-occurrence vector
Pattern Recognition Letters
International Journal of Biometrics
PSO versus AdaBoost for feature selection in multimodal biometrics
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Online joint palmprint and palmvein verification
Expert Systems with Applications: An International Journal
Feature level fusion of face and palmprint biometrics
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Robust linearly optimized discriminant analysis
Neurocomputing
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In the application of biometrics authentication (BA) technologies, the biometric data usually shows three characteristics: large numbers of individuals, small sample size and high dimensionality. One of major research difficulties of BA is the single sample biometrics recognition problem. We often face this problem in real-world applications. It may lead to bad recognition result. To solve this problem, we present a novel approach based on feature level biometrics fusion. We combine two kinds of biometrics: one is the face feature which is a representative of contactless biometrics, and another is the palmprint feature which is a typical contact biometrics. We extract the discriminant feature using Gabor-based image preprocessing and principal component analysis (PCA) techniques. And then design a distance-based separability weighting strategy to conduct feature level fusion. Using a large face database and a large palmprint database as the test data, the experimental results show that the presented approach significantly improves the recognition effect of single sample biometrics problem, and there is strong supplement between face and palmprint biometrics.