Authentication is the key parameter to speak the truth of an attribute claimed by the real entity. There are several ways to make authentication more robust and biometrics is one among them. From past decade, Biometric technology is widely adopted and accepted everywhere to authenticate an individual's identity. Also the adopted technology overcomes the limitations faced by the traditional authentication process such as knowledge based issues including password and token for the authentication of an individual. This paper makes a comprehensive study of the existing biometric methodologies, their usage and limitations that are employed in real time cases. It also presents the motivation for adapting biometrics in current situations. In addition to this, it also makes an attempt to talk on the technical and security related issues towards biometric systems.

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Bimodal biometric used to authenticate a person is more accurate compared to single biometric trait. In this paper we propose Feature Level Fusion based Bimodal Biometric using Transformation Domine Techniques (FLFBBT). The algorithm uses two physiological traits viz., Fingerprint and Face to identify a person. The Region of Interest (ROI) of fingerprint is obtained using preprocessing. The features of fingerprint are extracted using Dual Tree Complex Wavelet Transforms (DTCWT) by computing absolute values of high and low frequency components. The final features of fingerprint are computed by applying log on concatenated absolute value of high and low frequency components. The face image is preprocessed by cropping only face part and Discrete Wavelet Transforms (DWT) is applied. The approximation band coefficients are considered as features of face. The fingerprint and face image features are concatenated to derive final feature vector of bimodal biometric. The Euclidian Distance (ED) is used in matching section to compare test biometric in the database, it is observed that the values of EER and TSR are better in the case of proposed algorithm compared to individual transformation domain techniques.