# Cancelable Multimodal Biometrics Based on Chaotic Maps
###### Abstract
Several feature transformation techniques have been proposed such as biometric salting (a user-specific invertible transformation based on a password or key); robust hashing (a non-invertible transformation based on one-way functions); and cancelable biometrics. The major disadvantage of using raw biometric data--if these were compromised--is that they cannot be altered or canceled. However, cancelable template schemes allow non-invertible transforms to be employed without altering the biometric template space. Thus, using transformed or altered biometric data means that a stolen datum can be canceled and new distorted features can be created. This practice is referred to as cancelable biometrics, which is by far the main challenge when designing biometric template protection schemes [3]. Using cancelable biometrics, different templates can be created as a result of changing the parameters of the transformation functions, such that no matching should exist between the original and the transformed information. In [4; 5], the researchers have again clarified and analyzed this principle, which was later designed in a more general sense to designate all the biometric protection approaches that are based on a non-invertible transformation [2]. Chaotic maps were recently employed to develop biometric template protection schemes. Chaotic maps refer to the mathematical functions that reveal a chaotic behavior and create, based on initial conditions, a different sequence of numbers. These functions are highly sensitive to initial conditions, making their use very efficient in information security systems and specifically in the scrambling process. The present paper proposes using chaotic maps to develop a new hybrid cancelable multimodal biometric template protection approach that guarantees the requirements in terms of performance, security, diversity, and revocability. The main contribution of this research is combining feature transformation methods with cryptosystems by using chaotic maps, such as the Logistic map and Torus Automorphism. A chaotic map is utilized to create random chains, from which a key parameter can be introduced. Using these seemingly random chains, encrypted data are generated based on the structure of the permutation-substitution process, which is highly effective in creating confusion and diffusion characteristics. Using this approach, it should be computationally difficult for an adversary to retrieve any raw data regarding the system's genuine users in the case of the protected template being compromised. In addition, the proposed biometric system is built using in conjunction of both the fingerprint and the face of the individual, and combining matching scores of these two biometric modalities can significantly improve the reliability and the efficiency of the biometric system as two biometric sensors need to be attacked to gain illegal access. Moreover, the matching of the templates is performed in the transformed domain, which ensures that the original data are not disclosed to third parties, aiming to satisfy the revocability requirement. The remainder of the paper is arranged as follows. In Section 2, an overview of the related work is presented. Section 3 provides details of the proposed scheme. The experimental results are analyzed and discussed in Section 4. In the last section, the conclusion and future work are drawn.