Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

Abstract

Human recognition in a multi-biometric system is performed by combining biometric clues from different sources (multiple sensors, units, algorithms, samples and modalities) at different levels (sensor, feature, score, rank and decision level). Low computational complexity and adequate data for fusion make the score-level fusion a preferable option over other levels of fusion. However, incompatibility issue prevails at this level as scores obtained from different uni-biometric systems are disparate in nature. This disparity can be resolved by using score normalisation before fusion. This study first analysed the effect of generalised extreme value distribution-based score normalisation technique on different fusion techniques and then proposes an efficient score fusion technique based on Dezert-Smarandache theory (DSmT). A unique blend of belief assignment and decision-making methods in the DSmT framework is proposed for score-level fusion. For evaluation of the proposed method, experiments are performed on multi-algorithm, multi-unit and multi-modal biometrics systems created from three publicly available datasets: (i) NIST BSSR1 multi-biometric score database, (ii) face recognition grand challenge (FRGC) V2.0 and (iii) LG4000 iris dataset. Comparative studies of performance analysis show the efficiency of the authors' proposed method over other recently published state-of-the-art fusion methods.

Optimized multi‐biometric enhancement analysis

“…Multi-spectral palmprint Different t-conorm compared to sum, SVM [8] Iris, face GA features selectionSVM score fusion [9] Speech, lipreading Average [30] NIST BSSR1 multi-biometric score database (fingerprint and face), face, iris dataset Dezert-Smarandache theory DSmT [15] Palm/phalanges print Sum, product, min, max, hamacher t-norm, frank t-norm, yager's t-norm [5] Face, palmprint, signature, speech Sum rule compared to the unimodal system [31] Fingerprint, iris, left ear and right ear DE, exponent control, and Kernel mapping [19] Fingerprint and voice DE and proportional conflict redistribution [20] Face and voice PSO, Dempster Shafer and Belief functions [21] Two face databases Quasi-convex optimization [22] Iris, finger vein and fingerprint Evolutionary backtracking search optimization algorithm [25] Abbreviation: GA, Genetic Algorithm. only one contains two traits (NIST BSSR1), and the two other databases (FRGC, IRIS dataset) are used for multi-algorithm and multi-sample fusion (Table 2).…”

“…However, due to the limited number of tests, the work does not provide a significance test to confirm the score level superiority. A recent work proposes a unique blend of belief assignment and decision‐making methods in Dezert‐Smarandache theory framework [15]. They tested fusion of different datasets where only one contains two traits (NIST BSSR1), and the two other databases (FRGC, IRIS dataset) are used for multi‐algorithm and multi‐sample fusion (Table 2).…”

“…The score level fusion is the most cited and used fusion [8, 9, 15, 30, 31], which is due to its low computational complexity. In addition, comparative studies have shown that fusion at the score level outperforms other levels [15].…”

“…The score level fusion is the most cited and used fusion [8, 9, 15, 30, 31], which is due to its low computational complexity. In addition, comparative studies have shown that fusion at the score level outperforms other levels [15]. The proposition of [19] discusses the use of differential evolution, which has a similar structure to GA, in parameters tuning to reduce the overlapping of genuine and impostor distribution.…”

“…Consequently, assessing the efficiency of a fusing strategy requires testing the strategy on different score combinations before concluding whether the fusion method can outperform individual accuracy of the baseline systems. However, most of the research found in the literature compares fusion methods relying on their own baseline systems [3,15,16]. However, this cannot affirm whether the enhancement occurs as a result of the fusion or the used baseline systems' performances.…”