Multi-Modal Biometric Template Security: Fingerprint and Palmprint Based Fuzzy Vault

Multi-biometric system stores multiple templates for the same user corresponding to the different biometric sources. Infallible security should be provided to the stored biometric templates as biometric is not revocable. In this work, multi-modal biometric template security for palmprint and fingerprint is proposed which is based on the fuzzy vault generation. At first, the preprocessing steps are applied and subsequently, the features are extracted and combined. For recognition, we match the feature vectors of images. The multi-modal biometric template along with the input key are used to generate the fuzzy vault. In the decoding process, the template is given as input and is combined with the stored fuzzy vault to generate the corresponding final key. The experimentation is carried out using CASIA database for palmprint and FVC 2004 database for fingerprint. The evaluation metrics have FMR and FNMR value parameters. Citation: Brindha VE, Natarajan AM (2012) Multi-Modal Biometric Template Security: Fingerprint and Palmprint Based Fuzzy Vault. J Biom Biostat 3:150. doi:10.4172/2155-6180.1000150 J Biom Biostat ISSN:2155-6180 JBMBS, an open access journal Page 2 of 6 Volume 3 • Issue 6 • 1000150 used method for identity. Even identical twins have unique fingerprints. Passwords, PIN codes and smartcards are being used for identification, which may be lost or forgotten. But fingerprints can neither be lost nor forgotten, nor can be stolen. In recent years, a series of automated biometric-based identification methods have emerged which include fingerprint, palmprint, iris pattern, voice, etc. In these biometric features, palmprint has greater similarity to fingerprint whose identification is rather mature. Palmprint refers to an image acquired of the palm region of the hand. Palmprint based systems, as a new member in biometric family, has become an active research topic in recent years. The main advantage of palmprint is the availability of large space for extracting biometric features. Usually palmprint images should be normalized and oriented before feature extraction. It contains more information than fingerprints, so they are more distinctive. Palmprint capture devices are much cheaper than other devices like iris. Palmprints contain additional distinctive features such as wrinkles, principal lines which can be extracted from low-resolution images. By combining all features of palm and fingerprint such as ridge and valley features, principal lines and wrinkles, it is possible to build a highly accurate biometric system. Palmprint is a combination of mainly two features, namely, the palmar friction ridges and the palmar flexion creases [3]. The corrugated skin patterns with sweat glands are known as palmar friction ridges. Discontinuities in the epidermal ridge patterns are called the palmar flexion creases. Further, there are three palmprint regions like interdigital, thenar and hypothenar as shown in Figure 1. Palmprint has one salient feature called crease (shown in Figure 2). In the palmprint, the creases are large in number and comparatively easy to extract [3]. Creases are also approximately stable in a person’s whole life, which qualifies themselves as features in palmprint recognition. To deal with creases in palmprints, both the ridge direction and frequency of minutiae points are calculated which helps to reduce the computational complexity information about the ridges and other minutiae in the neighborhood of a minutia.