22-12-2012, 06:51 PM
Biometric System Laboratory
[attachment=45041]
The Handbook of Fingerprint Recognition
The book includes results of BioLab research and provides an updated snapshot of the current state-of-the-art in fingerprint recognition
Fingerprints: minutiae detection
The basic idea is to follow the ridge lines on the gray-scale image, by "sailing" according to the fingerprint directional image. A set of starting points is determined by superimposing a square-meshed grid on the gray-scale image. For each starting point, the algorithm keeps following the ridge lines until they terminate or intersect other ridge lines.
Fingerprints: MCC representation
MCC (Minutia Cylinder Code) is a novel minutiae representation and matching techniques
Fast and accurate
Bit-based, portable on light architectures
Suitable for template protection techniques
Fingerprints: indexing
Exclusive classification is not a good indexing method for retrieval on large databases:
the number of classes is small
fingerprints are non-uniformly distributed
“ambiguous” fingerprints cannot be reliably assigned to a unique class
Continuous classification associates a multidimensional point to each fingerprint and uses spatial queries for fingerprint retrieval by similarity.
Fingerprints: synthetic generation
Collecting large databases of fingerprint images is:
expensive both in terms of money and time
boring for both the people involved and for the volunteers, which are usually submitted to several acquisition sessions at different dates
problematic due to the privacy legislation which protects such personal data
Fingerprints: fake detection
One of the most recent challenges: fake finger detection
Two novel approaches (to be published in 2006)
Distortion analysis (Patent IT #BO2005A000399)
The user is required to place a finger onto the scanner surface and to apply some pressure while rotating the finger
Odor analysis (Patent IT #BO2005A000398)
Using one or more odor sensors (electronic noses) to detect materials usually adopted to make fake fingers
Fingerprint scanners: operational quality
How to evaluate the impact of each quality parameter (e.g. acquisition area, resolution accuracy, MTF) on the matching performance?
Operational fingerprint scanner quality
The ability of acquiring images that maximize the accuracy of automated fingerprint recognition systems
A large experimentation to understand the effects of the various quality parameters has been carried out
Performance evaluations: FVC
FVC is a technology evaluation of algorithms
Not complete systems, but only algorithms
Not a performance evaluation in a real application
Main aims
Track the state-of-the-art in fingerprint recognition
Provide updated benchmarks and a testing protocol for fair and unambiguous evaluation of fingerprint verification algorithms
Collaboration with the Italian government
BioLab scientifically supports the Italian National Centre for Information Technology in the Public Administration (CNIPA) within the established “Task Force on Biometrics” to:
Provide guidelines and support to the PA, and in particular to: Istituto Poligrafico e Zecca dello Stato, Min. Giustizia, Min. Interni, Min. Esteri, Esercito, ...
Test and certify biometric solutions
BioLab members are coauthors of the following “Quaderni CNIPA”:
N.9: Linee guida per l’impiego delle tecnologie biometriche nelle pubbliche amministrazioni
N.17: Linee guida per l’impiego delle tecnologie biometriche nelle pubbliche amministrazioni. Indicazioni operative
[attachment=45041]
The Handbook of Fingerprint Recognition
The book includes results of BioLab research and provides an updated snapshot of the current state-of-the-art in fingerprint recognition
Fingerprints: minutiae detection
The basic idea is to follow the ridge lines on the gray-scale image, by "sailing" according to the fingerprint directional image. A set of starting points is determined by superimposing a square-meshed grid on the gray-scale image. For each starting point, the algorithm keeps following the ridge lines until they terminate or intersect other ridge lines.
Fingerprints: MCC representation
MCC (Minutia Cylinder Code) is a novel minutiae representation and matching techniques
Fast and accurate
Bit-based, portable on light architectures
Suitable for template protection techniques
Fingerprints: indexing
Exclusive classification is not a good indexing method for retrieval on large databases:
the number of classes is small
fingerprints are non-uniformly distributed
“ambiguous” fingerprints cannot be reliably assigned to a unique class
Continuous classification associates a multidimensional point to each fingerprint and uses spatial queries for fingerprint retrieval by similarity.
Fingerprints: synthetic generation
Collecting large databases of fingerprint images is:
expensive both in terms of money and time
boring for both the people involved and for the volunteers, which are usually submitted to several acquisition sessions at different dates
problematic due to the privacy legislation which protects such personal data
Fingerprints: fake detection
One of the most recent challenges: fake finger detection
Two novel approaches (to be published in 2006)
Distortion analysis (Patent IT #BO2005A000399)
The user is required to place a finger onto the scanner surface and to apply some pressure while rotating the finger
Odor analysis (Patent IT #BO2005A000398)
Using one or more odor sensors (electronic noses) to detect materials usually adopted to make fake fingers
Fingerprint scanners: operational quality
How to evaluate the impact of each quality parameter (e.g. acquisition area, resolution accuracy, MTF) on the matching performance?
Operational fingerprint scanner quality
The ability of acquiring images that maximize the accuracy of automated fingerprint recognition systems
A large experimentation to understand the effects of the various quality parameters has been carried out
Performance evaluations: FVC
FVC is a technology evaluation of algorithms
Not complete systems, but only algorithms
Not a performance evaluation in a real application
Main aims
Track the state-of-the-art in fingerprint recognition
Provide updated benchmarks and a testing protocol for fair and unambiguous evaluation of fingerprint verification algorithms
Collaboration with the Italian government
BioLab scientifically supports the Italian National Centre for Information Technology in the Public Administration (CNIPA) within the established “Task Force on Biometrics” to:
Provide guidelines and support to the PA, and in particular to: Istituto Poligrafico e Zecca dello Stato, Min. Giustizia, Min. Interni, Min. Esteri, Esercito, ...
Test and certify biometric solutions
BioLab members are coauthors of the following “Quaderni CNIPA”:
N.9: Linee guida per l’impiego delle tecnologie biometriche nelle pubbliche amministrazioni
N.17: Linee guida per l’impiego delle tecnologie biometriche nelle pubbliche amministrazioni. Indicazioni operative