Age Factors in Biometric Processing by Michael Fairhurst

By Michael Fairhurst

As biometric-based identity and id authentication turns into more and more common of their deployment, it turns into very important to contemplate matters in terms of reliability, usability and inclusion. elements that are rather very important during this context are the connection among the character of the measurements extracted from a specific biometric modality, the age of the pattern donor, and the impact which age has on physiological and behavioral features invoked in a biometric transaction.

Topics comprise:
* overview of aging and biometrics
* social elements in growing old and their relevance to biometrics
* utilizing age to augment functionality in biometrics
* knowing growing older procedures in key biometric modalities
* modality-related methods to administration of age factors
* business point of view on biometric age factors
* age prediction from biometrics data
* databases and analytical implications
* usability and ageing
* getting old results and implications for biometric cryptosystems
* destiny demanding situations for platforms supply.

Age elements in Biometric Processing presents an outline of this subject for the quickly increasing neighborhood of stakeholders in biometrics-based id strategies in academia, and govt.

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2007): Includes 8 biometric traits (face, speech, fingerprints, hand, iris, signature, handwritting and keystroking) of 400 subjects (not all subjects provided all traits). The data were collected in four sessions within a 4 month time span. The age distribution of the subjects is 18–25 ) 42%, 25–35 ) 22%, 35–45 ) 16%, >45 ) 20%. BIOSEC (Fie´rrez-Aguilar et al. 2007): Includes 4 biometric traits (face, speech, fingerprints and iris) of 250 subjects (the extended version) and was acquired under the BioSec Integrated Project.

Suitable ageing datasets should have the following features: (I) (II) (III) They contain samples for numerous subjects. For each subject there should be multiple templates obtained at different ages. Non-ageing related types of variability should not be present in the samples. This is a key difficulty as ageing datasets with samples taken at different ages, most probably, were captured with different equipment and standards. For example, in the case of face images, images showing an 80-year-old during his/her childhood were captured about 70 years ago using different photographic technologies than images of the same person captured today.

J. (1997): ‘Speech production of normally aging adults’. Seminars in Speech and Language, 18(2), 135–141. Bowyer K. , Flynn P. J. (2007): ‘Image understanding for iris biometrics: A survey’. Computer Vision and Image Understanding, 110, 281–307. , Demirjian A. (2004): ‘Dental maturity curves in Finnish children: Demirjian’s method revisited and polynomial functions for age estimation’. Journal of Forensic Sciences, vol. 49, issue 6, pp. 1324–1331. Cross J. , Smith C. L. (1995): ‘Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification’.

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