Handbook of Fingerprint Recognition

By Davide Maltoni, Dario Maio, Anil K. Jain, Salil Prabhakar. Springer Professional Computing, 2005

4,113 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e83a2fa459ba921fb176beacba96038a502ff64d

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In Signal Processing

Citations in the corpus

Inference for empirical Wasserstein distances on finite spaces (2016, background): “…and recognition of synthetic fingerprints is a topic of great interest in forensic science and current state of the art methods (Cappelli et al., 2000) produce synthetic fingerprints that even human experts fail to recognize as such (Maltoni et al. (2009), page 292 and following feature).”
Fingerprint recognition by combining global structure and local cues (2006, methodology): “OWADAYS, more and more important applications are based on fingerprint recognition [especially the automatic fingerprint identification system (AFIS)] [1], [2], such as electronic personal identification card, e-commerce and various items on the privacy and security of information.”
Efficient Privacy-Preserving Biometric Identification (2011, neutral): “[12] provides more comprehensive information.”
Introduction to Biometrics (2011, background): “always synonymous with FAR and FRR, respectively (see [20] and [ 19 ]).”
50 Years of object recognition: Directions forward (2013, background): “Biometric vision-based systems for fingerprint recognition [17], iris pattern recognition [18], as well as finger-vein and palm-vein patterns [19, 20] have also gained acceptance by the law enforcement community and are widely used.”
Online finger-knuckle-print verification for personal authentication (2010, background): “Fingerprint [1-3], palmprint [10-16], hand geometry [17-19], hand vein [20-21], and inner-knuckle-print [28-29] have been proposed and well investigated in the literature.”
Fingerprint Classification Based on Depth Neural Network (2014, background): “The results of training are satisﬁed.”
Practical Biometric Authentication with Template Protection (2005, background): “iv) Many biometric identifiers can be forged based on template information [MMJ03].”
FVC2004: Third Fingerprint Verification Competition (2004, methodology): “Four new databases were collected using three commercially available scanners and the synthetic generator SFinGe [1], [7] (see section 3).”
A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation (2019, methodology): “[13] categorized these fingerprint features into three levels.”
uWave: Accelerometer-based personalized gesture recognition and its applications (2009, methodology): “While many paradigms exist for user authentication, including password [19], biometrics [16–18], speech [20], and handwriting [21], accelerometer-based gesture recognition has its unique value for user authentication because of its low cost, high efficiency, and no change to the form factors of the ”
Biometric cryptosystems: issues and challenges (2004, methodology): “Biometrics are not secrets and are not revocable [31]; while revocability and secrecy have been critical requirements of conventional cryptosystem design, one then wonders whether it is possible to design a secure authentication system from the system components which in themselves are neither secre”
Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory (2009, positive): “Law enforcement applications also involve identiﬁcation using rolled and partial ﬁngerprints obtained from diﬀerent surfaces [2].”
Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition (2010, background): “Although very effective solutions are currently available, fingerprint recognition cannot be considered a fully solved problem, and the design of accurate, interoperable, and computationally light algorithms is still an open issue [1].”
Performance evaluation of fingerprint verification systems (2006, neutral): “…statistics (e.g., template size, memory usage) cannot be obtained, and 3) there is no way to prevent score normalization and template consolidation [20], [16] (i.e., techniques where information from previous comparisons are unfairly exploited to increase the accuracy in successive comparisons).”
Towards anomaly detection for increased security in multibiometric systems: Spoofing-resistant 1-median fusion eliminating outliers (2014, background): “A more detailed description of fingerprint processing can be obtained from [8].”