Direct pore-based identification for fingerprint matching process

Abstract

Fingerprint, is considered one of the most crucial scientific tools in solving criminal cases. This biometric feature is composed of unique and distinctive patterns found on the fingertips of each individual. With advancing technology and progress in forensic sciences, fingerprint analysis plays a vital role in forensic investigations and the analysis of evidence at crime scenes. The fingerprint patterns of each individual start to develop in early stagesof life and never change thereafter. This fact makes fingerprints an exceptional means of identification. In criminal cases, fingerprint analysis is used to decipher traces, evidence, and clues at crime scenes. These analyses not only provide insights into how a crime was committed but also assist in identifying the culprits or individuals involved. Computer-based fingerprint identification systems yield faster and more accurate results compared to traditional methods, making fingerprint comparisons in large databases easier. These systems establish connections between fingerprints found at crime scenes and potential suspects' fingerprints, ensuring accurate results in forensic investigations. Furthermore, fingerprint analysis is not limited to criminal identification. It also proves highly useful in situations like identifying missing persons or victims and locating individuals lost in disaster areas. Therefore, fingerprint analysis has become an indispensable tool for justice systems, security agencies, and related institutions. Fingerprints are complex and unique characteristics that can uniquely identify individuals. This identification process typically consists of three levels, involving the features of ridge patterns, minutiae points, and pores. In the first level, the focus is on the ridge patterns present on the fingertips of each individual. Ridge patterns are raised areas on the fingertip surface and possess distinct shapes and flow directions for each person. These flow directions and shapes play a significant role in distinguishing one person's fingerprint from another's. In the second level, minutiae points are examined. Minutiae points are locations where ridge patterns intersect or bifurcate. These points can take various forms such as ridge endings, bifurcations, or ridge dots. Minutiae points are also utilized to determine the unique features of a fingerprint. The distinctiveness between two fingerprints often arises from differences in the types and positions of these points. The third level involves considering pores. Pores are tiny holes on the fingertip surface and have unique distributions in each fingerprint. The location and number of these holes are also used for fingerprint identification. These three levels constitute the fundamental components of fingerprint identification. This detailed examination and analysis demonstrate the high uniqueness of fingerprints and their effectiveness as a reliable biometric method for identity verification. However, there are some limitations and challenges faced by fingerprint identification technology. One primary issue is the potential inability of imaging systems to capture fingerprints with sufficient clarity and detail. Such a process can complicate data collection for imaging systems and consequently hinder providing a complete and accurate representation of fingerprints. Secondly, the insufficient level of porosity details for the third-level identification limits the widespread use of this form of identification. These factors highlight the challenges faced by fingerprint recognition technology. It's essential to enhance the sensitivity of imaging systems to obtain clearer and more detailed images even under varying conditions. Additionally, developing algorithms and data analysis methods to capture and process pore details more reliably is crucial. These solution-oriented approaches can unleash the potential of third-level identification, improving the reliability and widespread use of fingerprint recognition technology. Preserving pore-based fingerprint records in the unsolved cases database without subjecting them to any objective evaluation criteria underscores the importance of potential research in fingerprint analysis and identification. In this context, the presence of missing information in the said database emphasizes the inevitability of in-depth research in this field. Fingerprints captured in image records based on pores alone contain critical data reflecting individuals' unique biometric features. However, storing this data without subjecting it to any assessment process is of great significance for developing new methods and optimizing current technologies in the field of fingerprint analysis and identification. This way, the existence of incomplete data in the database highlights the necessity for comprehensive research in this area. Such gaps pave the way for the emergence of novel approaches and techniques, thereby enhancing the accuracy, precision, and reliability of fingerprint analysis. In this context, systematic examination and analysis of image records based on pores in the database are crucial for the development and reinforcement of fingerprint-based identification systems. The scientific community can focus on creating new algorithms and methods using this missing data, taking steps toward improving the forensic investigation process and finding more effective solutions in the field of security. Preserving image records based on pores in the unsolved cases database without subjecting them to evaluation criteria is the cornerstone of progress in fingerprint analysis and identification. Careful examination of these records can contribute to the development of more reliable and effective security applications. The research presented offers an innovative approach that goes beyond traditional fingerprint identification methods. In this study, an original dataset was created using a hyperspectral imaging system called "DocuCenter NIRVIS" and the "Projectina Image Acquisition-7000" software, where pores are more thoroughly examined compared to classical fingerprint identification methods. This dataset served as a foundation for a direct pore-based identification system developed for fingerprint matching. While traditional fingerprint identification methods generally rely on general skin surface characteristics, this research emphasizes the focus on pores using hyperspectral imaging. Hyperspectral imaging provides high-resolution images at different wavelengths, allowing for more detailed pore features to be captured. This enabled a more precise analysis of the unique pore patterns of each individual's fingerprint, leading to more reliable identification results. The DocuCenter NIRVIS device and the Projectina Image Acquisition-7000 software created a unique dataset using this hyperspectral imaging approach. This dataset includes hyperspectral images of fingerprints from different individuals, capturing data that defines the unique features of each pore. This dataset forms the basis for the development and testing of a direct pore-based identification system for fingerprint matching. The research presented offers a novel approach aiming to overcome the limitations of traditional fingerprint identification methods and provide a more accurate, precise, and reliable identification system. In this study, pores within the created dataset of 1050 fingerprint images were manually marked using the "Computer Vision Annotation Tool." This stage involves identifying and labeling each pore on each fingerprint. Manual marking was chosen to ensure the accurate and precise identification of pores. Following pore marking, an iterative nearest neighbor algorithm-based scoring system was applied. This algorithm identifies similar pore patterns by comparing different fingerprints within the dataset. This enables the matching of the pores contained in one fingerprint with similar pore patterns in other fingerprints. This step allows for the determination of the unique features of each pore and the comparison of various fingerprints. The scoring system evaluates the similar pore matches identified by the iterative nearest neighbor algorithm. A score is calculated for each match, indicating the degree of similarity between the pores. High-scoring matches are used to identify common pore patterns between different fingerprints. This stage is critical for making more precise matches between different fingerprints and identifying crime scene fingerprints. The research ensures that manually marked pores are successfully analyzed using the iterative nearest neighbor algorithm-based scoring system. This approach not only results in more accurate fingerprint matching but also represents a crucial step in identifying crime scene fingerprints. According to the findings obtained, there is a direct correlation between the number of analyzed pores and the accuracy of marking. These results clearly demonstrate that more detailed and accurate marking of pores directly impacts the reliability and success of matches. The results indicate that increasing the number of analyzed pores and enhancing the accuracy of their marking led to a significant increase in match scores. In other words, more detailed and accurate identification of each pore on each fingerprint has improved the accuracy and reliability of fingerprint matching. These findings demonstrate that precise marking of pores improves the detection of similar patterns and enhances the accuracy of the matching algorithm, leading to more reliable results.Additionally, the query results indicate that the scores for subsequent ranked fingerprint images in the database are notably lower after matching fingerprints. This emphasizes the effectiveness and superiority of the proposed pore-based identification approach compared to other methods. The results show that high scores are achieved for matching fingerprints, while the scores for subsequent fingerprint images in the database are notably lower. This observation highlights the effectiveness of the proposed pore-based approach in identifying and matching unique pore patterns more effectively than other methods. As the study highlights, the detailed analysis of pores and the accurate identification of similar pore patterns contribute to enhancing the accuracy and reliability of fingerprint identification. Therefore, the developed pore-based approach has the potential to yield more accurate, precise, and reliable results in fingerprint identification.