The deployment of fingerprint recognition systems has always raised concerns related to personal privacy. A fingerprint is permanently associated with an individual and, generally, it cannot be reset if compromised in one application. Given that fingerprints are not a secret, potential misuses besides personal recognition represent privacy threats and may lead to public distrust. Privacy mechanisms control access to personal information and limit the likelihood of intrusions. In this paper, image- and feature-level schemes for privacy protection in fingerprint recognition systems are reviewed. Storing only key features of a biometric signature can reduce the likelihood of biometric data being used for unintended purposes. In biometric cryptosystems and biometric-based key release, the biometric component verifies the identity of the user, while the cryptographic key protects the communication channel. Transformation-based approaches only a transformed version of the original biometric signature is stored. Different applications can use different transforms. Matching is performed in the transformed domain which enable the preservation of low error rates. Since such templates do not reveal information about individuals, they are referred to as cancelable templates. A compromised template can be re-issued using a different transform. At image-level, de-identification schemes can remove identifiers disclosed for objectives unrelated to the original purpose, while permitting other authorized uses of personal information. Fingerprint images can be de-identified by, for example, mixing fingerprints or removing gender signature. In both cases, degradation of matching performance is minimized.
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.