A wide variety of systems require reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their ser- vices.[1] The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user, and not anyone else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones and ATMs.[1] In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or simply biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. [4] By using bio- metrics it is possible to confirm or establish an individual’s identity based on who she is, rather than by what she possesses (e.g., an ID card) or what she remembers (e.g., a password). Unimodal biometric systems have to contend with a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates.[2] Some of these limitations can be addressed by deploying multimodal biometric systems that integrate the evidence presented by multiple sources of information. Thisseminar, there will be a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.[6] The various scenarios those are possible in multimodal biometric systems, the levels of fusion that are plausible and the integration strategies that can be adopted to consolidate information. Recognition, genetic algorithm, Spoof Attack in Multimodal biometrics

Biometrics, iris recognition, fingerprint, spoof attack, genetic algorithm