Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

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Wild, Peter ; Radu, Petru ; Chen, Lulu ; Ferryman, James (2016)
  • Publisher: Elsevier
  • Journal: Pattern Recognition, volume 50, pages 17-25 (issn: 0031-3203)
  • Related identifiers: doi: 10.1016/j.patcog.2015.08.007
  • Subject: Computer Vision and Pattern Recognition | Software | Signal Processing | Artificial Intelligence

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of livenessrecognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay- Attack Database and CASIA Face Anti-Spoofing Database), and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.
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