A longitudinal study of iris recognition in children

Biometrie recognition is dependent on the permanence of the biometrie characteristics over long periods of time. However, there has been limited research in this area, particularly in children during development. This paper presents the start of a longitudinal study of irises in children to understand when biometrics can be used reliably and the effect aging has on the biometric modality as a child grows. Data was collected and analyzed in children ages 4–12 years over three visits, spaced approximately six months apart. This is one of the few iris collections spanning this broad age range in children. The results show that there is a slight decrease in match scores between the resultant comparison of collection 1 to collection 3(12 months difference) and the resultant comparison of collection 1 to collection 2 (6 months difference); analysis shows this difference is not statistically significant. Additionally, the data analyzed resulted in very similar iris recognition performance when examining a subset of subjects in fifth grade and a subset of subjects in first grade. These results could indicate that the iris biometric characteristic is stable over time, at least as early as age 4, the youngest group tested in this work. Additional longitudinal data is needed to support this hy-pothesis.

[1]  Patrick J. Flynn,et al.  Template Aging in Iris Biometrics , 2013, Handbook of Iris Recognition.

[2]  Axel Munk,et al.  Modeling the Growth of Fingerprints Improves Matching for Adolescents , 2011, IEEE Transactions on Information Forensics and Security.

[3]  Anil K. Jain,et al.  Recognizing infants and toddlers using fingerprints: Increasing the vaccination coverage , 2014, IEEE International Joint Conference on Biometrics.

[4]  Anil K. Jain,et al.  Fingerprint Recognition of Young Children , 2017, IEEE Transactions on Information Forensics and Security.

[5]  Timothy F. Cootes,et al.  Modeling the process of ageing in face images , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  George W. Quinn,et al.  IREX VI : Temporal Stability of Iris Recognition Accuracy , 2013 .

[7]  Rama Chellappa,et al.  Computational methods for modeling facial aging: A survey , 2009, J. Vis. Lang. Comput..

[8]  Rama Chellappa,et al.  Modeling Age Progression in Young Faces , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Schumacher Guenter,et al.  Fingerprint Recognition for Children , 2013 .