Biometric System is used for person's recognition and identification for various applications. The Biometric system is unimodal and multimodal biometric system. Unimodal Biometric suffers from Noisy data, Intra class variation, non versality, spoofing etc. These drawbacks can remove by using Multimodal Biometric system. We developed the multimodal Biometric system by using Face and fingerprint Multimodalities. This system takes the advantage of individual Biometric System. This paper presents the fusion of face and fingerprint modalities at score level fusion. The system extracts the features and these features are then used for matching. Euclidean distance matcher is used for Face and Finger print modalities. Fingerprint recognition can be done with the help of minutiae matching and Gabor filter. The Face feature is extracted with the help of PCA (Principle Component Analysis) for dimensionality Reduction.Then the match scores are Normalized and sum score level fusion is used to develop the system. The proposed approach provides the better results. The Recognition Rate is increased and the error rate is decreased by with the help of this system.

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Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, and unacceptable error rates. To represent the large amount of data in the biometric images an efficient feature extraction method is needed. This paper presents the feature extraction of fingerprint image processing stages such as image Preprocessing, Converting the image in to gray scale, Image Enhancement can be performed with the help of Discrete Fourier transformation, image Binarization, image segmentation, image thinning, Minutiae Extraction after Minutiae Extraction again segmentation technique is used .In finger print images there are foreground regions and background regions where foreground region show the ridges and valleys while the background regions are to be left out. .The foreground regions have high value while the background regions have low values. Segmentation separates the foreground region from the background image for the reliable extraction of minutiae. The feature extraction is the first step used for matching used in biometric recognition. After feature extraction the next step is preprocessing of Minutiae. The extracted feature is called as templates, which is used in matching.

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