Implementation of multimodal biometric authentication using soft computing techniques

Identity Management refers to a challenge of providing authorized users with secure and easy access to information and services for personal identification system. The objective is to implement a secure identification system for determining an individual´s identity. Conventional methods for personal identification include keys, tokens, access cards, PINs and passwords which leads to drawbacks such as stealing, duplicating, cracking and sharing. In order to overcome the drawbacks, biometrics based identification is implemented. Nevertheless, unimodal biometrics is suffered due to noise, intra class variations, spoof attacks, non-universality. To avoid these attacks, multimodal biometrics which is the combination of more modalities is adapted. Fingerprint and Palmprint identification are the emerging and leading technologies used as a source of authentication. Neural Networks one of the Soft Computing techniques has been proposed for feature extraction and minutiae point detection. Euclidean distance values are used for minutiae matching. The proposed method achieves excellent identification rate and are more secure in noisy environment.