Face and palmprint feature level fusion for single sample biometrics recognition

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.