A Novel Serial Multimodal Biometrics Framework Based on Semisupervised Learning Techniques

We propose in this paper a novel framework for serial multimodal biometric systems based on semisupervised learning techniques. The proposed framework addresses the inherent issues of user inconvenience and system inefficiency in parallel multimodal biometric systems. Further, it advances the serial multimodal biometric systems by promoting the discriminating power of the weaker but more user convenient trait(s) and saving the use of the stronger but less user convenient trait(s) whenever possible. This is in contrast to other existing serial multimodal biometric systems that suggest optimized orderings of the traits deployed and parameterizations of the corresponding matchers but ignore the most important requirements of common applications. In terms of methodology, we propose to use semisupervised learning techniques to strengthen the matcher(s) on the weaker trait(s), utilizing the coupling relationship between the weaker and the stronger traits. A dimensionality reduction method for the weaker trait(s) based on dependence maximization is proposed to achieve this purpose. Experiments on two prototype systems clearly demonstrate the advantages of the proposed framework and methodology.