Some learning issues in user-specific multimodal biometrics

The idea of user-specific multimodal biometrics is pioneered by [1] and further exploited by [2] recently. In this paper, we look into several issues pertaining to user-specific multimodal biometric verification. These issues include the small sample size problem, learning of local decision hyperplanes and setting of local thresholds. For small sample size problem, the noise-injection technique and a feature scaling-space technique are considered. For local learning, we adopt a recently proposed reduced polynomial since it has fast single-step computation and accurate estimation. For setting of local decision thresholds, nine baselines are identified. Extensive experiments are performed on a moderate data set and relatively conclusive results are observed.