Multibiometrics can obtain a higher accuracy than the single biometrics by simultaneously using multiple biometric traits of the subject. We note that biometric traits are usually in the form of images. Thus, how to properly fuse the information of multiple biometric images of the subject for authentication is crucial for multibiometrics. We propose a novel image-based linear discriminant analysis (IBLDA) approach to fuse two biometric traits (i.e., bimodal biometric images) of the same subject in the form of matrix at the feature level. IBLDA first integrates two biometric traits of one subject into a complex matrix and then directly extracts low-dimensional features for the integrated biometric traits. IBLDA also enables more information to be exploited than the matching score level fusion and the decision level fusion. Compared to linear discriminant analysis (LDA), IBLDA has the following advantages: First, it can overcome the small sample size problem that conventional LDA usually suffers from. Second, IBLDA solves the eigenequation at a low computational cost. Third, when storing the scatter matrices IBLDA will not bring as heavy a memory burden as conventional LDA. We also clearly show the theoretical foundation of the proposed method. The experiment result shows that the proposed method can obtain a high classification accuracy.
- biometric images
- feature extraction
- feature level fusion
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics