title: Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.
abstract: Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong. menghui.lim@gmail.com
authors.forename: Meng-Hui
authors.initials: MH
authors.lastname: Lim
authors.forename: Andrew Beng Jin
authors.initials: AB
authors.lastname: Teoh
authors.forename: Kar-Ann
authors.initials: KA
authors.lastname: Toh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D016494:Computer Security; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted