A Gradient Descent Approach for Multi-modal Biometric Identification

While biometrics-based identification is a key technology in many critical applications such as searching for an identity in a watch list or checking for duplicates in a citizen ID card system, there are many technical challenges in building a solution because the size of the database can be very large (often in 100s of millions) and the intrinsic errors with the underlying biometrics engines. Often multi-modal biometrics is proposed as a way to improve the underlying biometrics accuracy performance. In this paper, we propose a score based fusion scheme tailored for identification applications. The proposed algorithm uses a gradient descent method to learn weights for each modality such that weighted sum of genuine scores is larger than the weighted sum of all the impostor scores. During the identification phase, top K candidates from each modality are retrieved and a super-set of identities is constructed. Using the learnt weights, we compute the weighted score for all the candidates in the superset. The highest scoring candidate is declared as the top candidate for identification. The proposed algorithm has been tested using NIST BSSR-1 dataset and results in terms of accuracy as well as the speed (execution time) are shown to be far superior than the published results on this dataset.