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Source code for fingerprint recognition, face recognition and much more

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Biometric systems make use of the physiological or behavioral traits of individuals, for recognition purposes. These traits include fingerprints, hand-geometry, face, voice, iris, retina, gait, signature, palm-print, ear, etc. Biometric systems that use a single trait for recognition (i.e., unimodal biometric systems) are often affected by several practical problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multimodal biometric systems overcome some of these problems by consolidating the evidence obtained from different sources. Researchers have shown that the use of multimodal biometrics provides better authentication performance over unimodal biometrics. Biometric fusion can be performed at image level, feature level, match score level, decision level, and rank level.

We have developed a multimodal biometric system that efficiently combines fingerprint, iris and palmprint recognition. Extracted features are combined and a final score is computed for classification. Code has been tested with CASIA Iris Image Database Version 1.0 and CASIA Palmprint Image Database. Fingerprint database used in our experiments was a collection of fingerprint images taken with an UPEK swipe fingerprint reader with capacitive sensor and USB 2.0 connection. Database is 16 fingers wide and 8 impressions per finger deep (totally 128 fingerprints). Other biometric modalities are available on request.

Index Terms: Matlab, source, code, multimodal, biometric, recognition, score, level, normalization.






Figure 1. Multimodal biometric

A simple and effective source code for Multimodal Biometric Recognition.

Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Signal Processing Toolbox and Matlab Wavelet Toolbox are required.

Major features


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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox, Matlab Signal Processing Toolbox and Matlab Wavelet Toolbox are required. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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