As biometric applications are gaining popularity, there is increased concern over the loss of privacy and potential misuse of biometric data held in central repositories. We present a biometric authentication framework that constructs a multi-biometric template by layering multiple biometrics of a user, such that it is difficult to separate the individual layers. Thus, the framework uses the biometrics of the user to conceal them among one another. The resulting biometric template is also cancelable if the system is implemented with cancelable biometrics, such as voice. We present a realization of this idea combining two or three different fingerprints of the user, using four different methods of template construction. Three of the methods use less and less information of the constituent biometrics, so as to lower the risk of leakage and cross-link rates. Results are evaluated on publicly available Finger Verification Championship (FVC) 2000, 2002 and NIST fingerprint databases. With the FVC databases, we obtain 2.1%, 3.9% and 3.4% Equal Error Rate on average using the three proposed methods, while the state-of-the-art commercial system achieves 1.9%. Furthermore, we show low cross-link rates under 63% under different scenarios, while genuine identification rates are 100%, with such a small gallery of 55 templates.

We examine the performance of multimodal biometric authentication systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometric systems on a population approaching 1,000 individuals. The majority of prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations of a few hundred users. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce new methods of normalization and fusion that further improve the accuracy.

In traditional cryptosystems, user authentication is based on possession of secret keys; the method falls apart if the keys are not kept secret (i.e., shared with non-legitimate users). Further, keys can be forgotten, lost, or stolen and, thus, cannot provide non-repudiation. Current authentication systems based on physiological and behavioral characteristics of persons (known as biometrics), such as fingerprints, inherently provide solutions to many of these problems and may replace the authentication component of traditional cryptosystems. We present various methods that monolithically bind a cryptographic key with the biometric template of a user stored in the database in such a way that the key cannot be revealed without a successful biometric authentication. We assess the performance of one of these biometric key binding/generation algorithms using the fingerprint biometric. We illustrate the challenges involved in biometric key generation primarily due to drastic acquisition variations in the representation of a biometric identifier and the imperfect nature of biometric feature extraction and matching algorithms. We elaborate on the suitability of these algorithms for digital rights management systems.

We propose a multimedia content protection framework that is based on biometric data of the users and a layered encryption/decryption scheme. Password-only encryption schemes are vulnerable to illegal key exchange problems. By using biometric data along with hardware identifiers as keys, it is possible to alleviate fraudulent usage of protected content. A combination of symmetric and asymmetric key systems is utilized for this purpose. The computational requirements and applicability of the proposed method are addressed. The results of encryption and decryption experiments related to time measurements are included. Watermarking systems can be used to complement the proposed method to permit novel uses of protected multimedia data.

With the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. The fact that biometric data is not replaceable and is not secret, combined with the existence of several types of attacks that are possible in a biometric system, make the issue of security/integrity of biometric data extremely critical. We introduce two applications of an amplitude modulation-based watermarking method, in which we hide a user's biometric data in a variety of images. This method has the ability to increase the security of both the hidden biometric data (e.g., eigen-face coefficients) and host images (e.g., fingerprints). Image adaptive data embedding methods used in our scheme lead to low visibility of the embedded signal. Feature analysis of host images guarantees high verification accuracy on watermarked (e.g., fingerprint) images.