By Michael Fairhurst
As biometric-based id and id authentication turns into more and more frequent of their deployment, it turns into vital to contemplate matters when it comes to reliability, usability and inclusion. components that are fairly vital 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.
* overview of ageing and biometrics
* social components in getting older and their relevance to biometrics
* utilizing age to reinforce functionality in biometrics
* knowing getting old methods in key biometric modalities
* modality-related techniques to administration of age factors
* commercial viewpoint on biometric age factors
* age prediction from biometrics data
* databases and analytical implications
* usability and ageing
* getting older results and implications for biometric cryptosystems
* destiny demanding situations for platforms supply.
Age components in Biometric Processing presents an outline of this subject for the speedily increasing neighborhood of stakeholders in biometrics-based identity recommendations in academia, and executive.
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Additional info for Age Factors in Biometric Processing
It has long been a guiding principle of system design that a system should be able to adapt to the needs of the user, rather than the other way round. With respect to a phenomenon such as natural ageing, this is surely a principle which researchers should strive to embrace and adopt as a real priority. References R. Cracknell. (2010). The ageing population. Available: http://www. uk/business/publications/research/key-issues-for-the-new-parlia ment/value-for-money-in-public-services/the-ageing-population/  G.
141–154. Lanitis A. (2008): ‘Comparative evaluation of automatic age progression methodologies’. EURASIP Journal on Advances in Signal Processing, Article ID 239480, 2008. 34 Age factors in biometric processing Lanitis A. (2010): ‘A survey of the effects of aging on biometric identity verification’. International Journal of Biometrics, vol. 2, issue 1, pp. 34–52. , Taylor C. , Cootes T. F. (2002): ‘Toward automatic simulation of aging effects on face images’. IEEE Transactions of Pattern Analysis and Machine Intelligence, vol.
However, in recent studies (Baker et al. 2009; Bowyer et al. 2007) the effects of both direct and indirect ageing effects on iris are highlighted. In a survey on iris-recognition state (Bowyer et al. 2007) it is stated that certain eye-related diseases such as cataract (Jaffe et al. 1990) and glaucoma (Rhee 2003) affect the appearance of the iris and consequently the accuracy of iris-based biometric identification systems. Usually diseases such as cataract and glaucoma appear in subjects belonging to older age groups; hence, this type of variation can be regarded as an ageing indirect one.
Age Factors in Biometric Processing by Michael Fairhurst