The quality of the biometric feature obtained after biometric image extraction and preprocessing improves classifier accuracy and determines the degree and standard of user authentication, to a large extent. Preprocessing is the process of preparing the input images (face or fingerprints) to be ready for the next step of the authentication system, in order to produce a good enough quality of output face or fingerprint image. In this paper, we present an efficient face and finger print image preprocessing using Enhanced Extracted Face (EEF) method and Plainarized Region of Interest (PROI) method respectively. The aim is to reduce one or more of the following -False accept rate (FAR), False reject rate (FRR), Failure to enroll rate (FTE) and increase accuracy and recognition speed.

The beginning of the 21st century was marked with events that focused on the world's attention to public security. Increase in technological advancement gave people possibilities of information transfer and ease of physical mobility unseen before. With those possibilities comes risk of fraud, theft of personal data, or even theft of identity. One of the ways to prevent this is through biometric authentication system. Unibiometric systems rely on the evidence of a single source of information whereas multibiometric systems consolidate multiple sources of biometric evidences. Multibiometric systems, if designed properly, are able to enhance the matching performance. In this paper, Intelligent Multi-Biometric Authentication System, face and fingerprint biometric traits are used. When the images are captured, preprocessing in face and fingerprint images is done using Enhanced Extracted Face (EEF) method and Plainarized Region Of Interest (PROI) method respectively. These are fed into a Cascaded Link Feed Forward Neural Network(CLFFNN) which is a classifier trained with backpropagation algorithm. CLFFNN comprises of CLFFNN(1) used for training and CLFFNN(2) used as the main classifier. They are arranged in cascades. Afterwards, both outputs from face and fingerprints are combined using AND operation.