Impact of artificial "gummy" fingers on fingerprint systems

By Tsutomu Matsumoto, H. Matsumoto, K. Yamada, S. Hoshino. 2002

989 citations recorded by the source corpus.

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In Signal Processing

Citations in the corpus

Biometric Authentication: A Review (2009, methodology): “Now live finger print readers based on optical, thermal, silicon or ultrasonic principles [1, 2, 3, 4] are used for this purpose.”
Fingerprint Liveness Detection Using Local Ridge Frequencies and Multiresolution Texture Analysis Techniques (2006, methodology): “Keywords: fingerprints, liveness, ridge frequency analysis, multiresolution texture analysis, first and second order statistical features, fuzzy c-means classifier.”
A Survey on Anti-Spooﬁng Schemes for Fingerprint Recognition Systems (2014, methodology): “Matsumoto et al. in [Matsumoto et al. 2002] described how to make molds from live ﬁngers and the corresponding artiﬁcial clones based on these molds.”
Robustness of multi-modal biometric verification systems under realistic spoofing attacks (2011, methodology): “For instance, a fake fingerprint or a photo of the targeted client [2, 3, 4].”
Generating Cancelable Fingerprint Templates (2007, background): “There have been several instances where artificial fingerprints [14] have been used to circumvent biometric security systems.”
Comparing passwords, tokens, and biometrics for user authentication (2003, background): “To date, many of these antiforgery methods have been defeated [22]; however, it is shortsighted to argue which side is currently winning, counterfeiters or anticounterfeiters.”
50 years of biometric research: Accomplishments, challenges, and opportunities (2016, neutral): “While Flom and Safir presented ideas for iris image capture, feature extraction, and matching, the first working iris recognition system was developed and implemented by John Daugman in the early 1990s [19] .”
Combining match scores with liveness values in a fingerprint verification system (2012, background): “Recent research has highlighted the vulnerability of biometric systems to spoof attacks, commonly realized by presenting duplicated biometric traits to the sensor [14].”
Guide to Biometrics (2003, background): “For other biometrics, impersonation is harder-though several groups [133] have reported success in defeating fingerprint systems with rubber casts of fingerprints.”
LBP − T OP based countermeasure against face spooﬁng attacks (2012, background): “[23], [14] and [18] indicate that fingerprint authentication systems suffer from similar weakness.”
Biometrics Evaluation Under Spoofing Attacks (2014, neutral): “Index Terms —Attack, counter-measures, counter-spooﬁng, disguise, dishonest acts, biometric veriﬁcation, forgery, liveness detection, replay, spooﬁng, evaluation, face recognition.”
Evaluation of multimodal biometric score fusion rules under spoof attacks (2012, methodology): “However experiments carried out in [5] using real spoofed fingerprints, showed that the resulting score distribution of fake fingerprints was considerably different from the genuine one.”