Fingerprint Recognition and Analysis

Abstract

Security systems are now computerized. Automated security systems are essential now. These days most of the banking transactions, use of cell phones and personal digital assistants (PDAs) are frequently performed. This rapid progress in personal communication system, wireless communication system and smart card technology in the society makes information more quick & realistic. Due to the growing importance of the information technology and the necessity of the protection and access restriction, reliable personal identification is necessary and essential condition also. There are three main methodologies when performing this verification. The security system can ask the user to provide some secret information known only to the user and the system or can ask the user to provide something only the user has access to, it could identify trait which is unique for the user. Identifying trait that is unique for the user is known as fingerprint recognition, hand geometry, iris, face and signature etc.

Reliable extraction of features from poor quality prints is the most challenging problem faced in the area of fingerprint recognition. Fingerprints are the oldest and most widely used form of biometric identification. Local characteristic called minutiae points represent fingerprints. This can be used as identification marks for fingerprint recognition. The goal of this thesis is to develop a complete system for fingerprint recognition through minutiae extracting and matching minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of image enhancement, image segmentation and binarization is first applied on fingerprints before they are evaluated. The combination of multiple methods comes from a wide investigation into research papers. Minutias marking with special consideration of the triple branch counting and false minutiae removal methods are used in the work. Also some novel changes like segmentation using morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work. The minutiae based fingerprints recognition technique is studied in detail and implemented in MATLAB.