Forensic science has developed significantly in the last few decades. Its key role is to provide crime investigators with processed data obtained from the crime scene to achieve more accurate results presented in court. Biometrics has proved its robustness against various critical crimes encountered by forensics experts. Fingerprints are the most important biometric used until now due to their uniqueness and production low cost. The automated fingerprint identification system (AFIS) came into existence in the early 1960s through the cooperation of the countries: USA, UK, France, and Japan. Ever since it started to develop gradually because of the challenges found at the crime scenes such as fingerprints distortions and partial cuts which in turn can severely affect the final calculations made by experts. The vagueness of the results was the main motivation to build a robust fingerprint identification system that introduces new and enhanced methods in its stages to help experts make more accurate decisions. The proposed fingerprint identification system uses Fourier domain analysis for image enhancement, then the system cuts the image around the core point after applying the rotation and core point detection methods. After that, it calculates the similarity based on the distance between fingerprint histograms extracted using the Local Binary Pattern (LBP). The system's last step is to translate the results into a sensible form where it utilizes fuzziness to provide more possibilities for the answer. The proposed identification system showed high efficiency on FVC 2002 and FVC 2000 databases. For instance, the results of applying our system on FVC 2002 provided a set of three ordered matching candidates such that 97.5 % of the results provided the correct candidate as the first order, and the rest of 2.5 % provided the correct candidate as the second order.

Biometrics refers to the science of measuring and analyzing biological or behavioral characteristics to identify people. It has been used in many fields including digital forensics, identification systems, and security. The most common biometric nowadays is fingerprint, which has improved significantly in the last two decades. Several issues in fingerprint identification and authentication systems have been raised due to factors such as displacement of the finger while scanning, fingerprint rotation, major cuts, distortions, and substandard quality images lowering the overall system efficiency. Evolutionary algorithms evolved in recent years to enhance the system performance over time. The proposed system uses a modified version of the bacterial memetic evolutionary algorithm to overcome the identification issues and to help and support forensic experts to make reliable decisions faster. The proposed system was evaluated on five different databases and the results demonstrated that the system succeeded in identifying the correct match from the first candidate in all cases among all examined databases.