Exploiting quality and texture features to estimate age and gender from fingerprints

Emanuela Marasco; Luca Lugini; Bojan Cukic

doi:10.1117/12.2048125

29 May 2014 Exploiting quality and texture features to estimate age and gender from fingerprints

Emanuela Marasco, Luca Lugini, Bojan Cukic

Proceedings Volume 9075, Biometric and Surveillance Technology for Human and Activity Identification XI; 90750F (2014) https://doi.org/10.1117/12.2048125
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

Age and gender of an individual, when available, can contribute to identification decisions provided by primary biometrics and help improve matching performance. In this paper, we propose a system which automatically infers age and gender from the fingerprint image. Current approaches for predicting age and gender generally exploit features such as ridge count, and white lines count that are manually extracted. Existing automated approaches have significant limitations in accuracy especially when dealing with data pertaining to elderly females. The model proposed in this paper exploits image quality features synthesized from 40 different frequency bands, and image texture properties captured using the Local Binary Pattern (LBP) and the Local Phase Quantization (LPQ) operators. We evaluate the performance of the proposed approach using fingerprint images collected from 500 users with an optical sensor. The approach achieves prediction accuracy of 89.1% for age and 88.7% for gender.

Citation Download Citation

Emanuela Marasco, Luca Lugini, and Bojan Cukic "Exploiting quality and texture features to estimate age and gender from fingerprints", Proc. SPIE 9075, Biometric and Surveillance Technology for Human and Activity Identification XI, 90750F (29 May 2014); https://doi.org/10.1117/12.2048125

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