Soft Biometric Traits for Personal Recognition Systems

  • Anil K. Jain
  • Sarat C. Dass
  • Karthik Nandakumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3072)


Many existing biometric systems collect ancillary information like gender, age, height, and eye color from the users during enrollment. However, only the primary biometric identifier (fingerprint, face, hand-geometry, etc.) is used for recognition and the ancillary information is rarely utilized. We propose the utilization of “soft” biometric traits like gender, height, weight, age, and ethnicity to complement the identity information provided by the primary biometric identifiers. Although soft biometric characteristics lack the distinctiveness and permanence to identify an individual uniquely and reliably, they provide some evidence about the user identity that could be beneficial. This paper presents a framework for integrating the ancillary information with the output of a primary biometric system. Experiments conducted on a database of 263 users show that the recognition performance of a fingerprint system can be improved significantly (≈ 5%) by using additional user information like gender, ethnicity, and height.


Recognition Performance Test User Biometric System False Acceptance Rate Ancillary Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jain, A.K., Bolle, R., Pankanti, S. (eds.): Biometrics: Personal Identification in Networked Security. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  2. 2.
    Hong, L., Jain, A.K., Pankanti, S.: Can Multibiometrics Improve Performance? In: Proceedings of IEEE Workshop on Automatic Identification Advanced Technologies, New Jersey, U. S.A, pp. 59–64 (1999)Google Scholar
  3. 3.
    Bertillon, A.: Signaletic Instructions including the theory and practice of Anthropometrical Identification, R.W. McClaughry Translation. The Werner Company (1896)Google Scholar
  4. 4.
    Heckathorn, D.D., Broadhead, R.S., Sergeyev, B.: A Methodology for Reducing Respondent Duplication and Impersonation in Samples of Hidden Populations. In: Annual Meeting of the American Sociological Association, Toronto, Canada (1997)Google Scholar
  5. 5.
    Wayman, J. L.: Large-scale Civilian Biometric Systems - Issues and Feasibility. In: Proceedings of Card Tech / Secur Tech ID (1997) Google Scholar
  6. 6.
    Givens, G., Beveridge, J.R., Draper, B.A., Bolme, D.: A Statistical Assessment of Subject Factors in the PCA Recognition of Human Subjects. In: Proceedings of CVPR Workshop: Statistical Analysis in Computer Vision (2003)Google Scholar
  7. 7.
    Newham, E.: The Biometrics Report. SJB Services (1995)Google Scholar
  8. 8.
    Jain, A.K., Lu, X.: Ethnicity Identification from Face Images. In: Proceedings of SPIE International Symposium on Defense and Security: Biometric Technology for Human Identification (2004) (to appear)Google Scholar
  9. 9.
    Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity authentication system using fingerprints. Proceedings of the IEEE 85, 1365–1388 (1997)CrossRefGoogle Scholar
  10. 10.
    Kim, J.S., et al.: Object Extraction for Superimposition and Height Measurement. In: Proceedings of Eighth Korea-Japan Joint Workshop on Frontiers of Computer Vision (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Anil K. Jain
    • 1
  • Sarat C. Dass
    • 2
  • Karthik Nandakumar
    • 1
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityUSA
  2. 2.Department of Statistics and ProbabilityMichigan State UniversityUSA

Personalised recommendations