Automated Fingerprint Identification System (AFIS)

Automated Fingerprint Identification System (AFIS)

The Automated Fingerprint Identification System (AFIS) is a biometric system that uses a computer database of fingerprint records to search and compare them in order to identify known and unfamiliar fingerprints. In a single second, modern AFISs can search over a billion fingerprint records. The present algorithms are nearly perfect in terms of accuracy. Law enforcement agencies typically employ automated fingerprint identification systems (AFIS) for criminal identification purposes, the most essential of which being identifying a person suspected of committing a crime or linking a suspect to other unsolved crimes. A similarly related approach is automated fingerprint verification, which is utilized in applications like attendance and access control systems. On a technological level, verification systems confirm a user's claimed identity, whereas identification systems determine to identify exclusively based on fingerprints. AFISs have been employed in large-scale civil identifications, with the main goal of preventing repeated enrollments in electoral, welfare, driver licensing, and other systems.

Trends in Fingerprint Technology

The biometric authentication procedure based on fingerprint matching is said to be the most successful and has become the latest trend around the world. It has become an indisputable parameter of identification because it is by far the most dependable and accurate method of recognizing a person. A biometric fingerprint reader records the impressions left by the patterns of ridges on a human's finger pads. A fingerprint is one-of-a-kind and belongs to only one person. As a result, it authenticates persons and records their imprints to be matched later if necessary, ensuring the safest and most reliable way of verification.

Companies are constantly developing new fingerprint readers based on updated technology in order to make the fingerprint recognition process easier and more convenient. These USB fingerprint readers use optical sensors that can detect the touch of a finger in their area and are shock, corrosion, and vibration resistant. The innovative technology assures that it can capture a crisp and distortion-free image of a finger in any condition, regardless of whether it is scarred, damp, or even old.

Fingerprinting is being used in a variety of settings. Fingerprinting is currently used at airports, border security, banks, schools, colleges, and offices. Many institutions, including hospitals, have begun to use fingerprint attendance. Hospitals have even included fingerprint matching as part of their access control procedures. As the most secure technique, biometric fingerprint readers are used to matching previously stored fingerprint data of doctors, nurses, and other medical workers and allow them admission. Some hospitals are even scanning and analyzing their employees' sub-thermal palm vein patterns these days. This ensures that even if there are cuts or abrasions on the skin, precise identification is accomplished.

Architecture of Fingerprint Matching System

The components of the fingerprint identification system include:

Enrolment Module: This module is responsible for registering a user's fingerprint. The Fingerprint Scanner scans the impression of the finger and generates a raw digital image.

Processing Module: The system's processing stage takes the data from the scanner and processes it further. Feature Extraction is performed on the fingerprint, and Feature Vectors are generated.

Database Module: The Database Module stores the User templates. The Processing Module's Feature Vector is evaluated against one or more existing templates.

Verification/Identification Module: This Module connects to the application system, allowing the User to assert his or her identity.

How Does Fingerprint Matching System Work?

In an automated fingerprint recognition system, fingerprint classification and matching are critical components. To establish if a plausible match exists, the fingerprint matcher compares features from the input search point to all suitable records in the database. There have been several approaches to automatic fingerprint matching proposed, including minutiae-based approaches and image-based approaches. Minutia-based techniques are the most common, with practically every modern fingerprint identification and verification system using them.

Fingerprint verification is primarily divided into two steps: Minutiae extraction and Minutiae matching. The first stage involves fingerprint sensing, which has traditionally been accomplished by inking the finger and pressing it on a paper card, which is subsequently scanned, yielding a digital image.

Off-line acquisition is the term for this method, which is still employed in law enforcement applications. At the moment, fingerprint images can be obtained by pushing the finger against the flat surface of an electronic fingerprint sensor. This is referred to as internet acquisition. The noise in the acquired image is eliminated during the pre-processing stage, and the minutiae are recovered from the pre-processed image. Passing minutiae patterns of the fingerprint to the matcher is the final stage in fingerprint matching. Based on fingerprint matching, this matcher will generate a match score.

Minutiae Extraction

Ridges, valleys, cores, deltas, and pores make up the basic fingerprint image. For minutiae-based matching, the ridge ends, and ridge bifurcations are employed to compare two fingerprints to each other. A pixel with one neighbour is commonly referred to as a ridge ending, while a pixel with three neighbours is referred to as a ridge bifurcation. Ridge ending and ridge bifurcation, often known as genuine minutiae, play an important part in fingerprint detection. Because the ridge ending and bifurcation do not alter over time, they are ideal for fingerprint matching. The average fingerprint has 40 to 100 minutiae points. A fingerprint image's coordinate location is used to show the minutiae location. Different systems depict the position of minutiae in different ways.

The conversion of a grayscale image into a binarized image, where black pixels indicate ridges and white pixels represent valleys, is required to detect minutiae from a fingerprint. Binarization is the term for the grayscale image conversion process. Each pixel in the image must be evaluated for white and black pixel assignment as part of this process. To determine ridge ends and ridge bifurcation, the binary picture is first scanned. The scanning of patterns is done both horizontally and vertically. For scanning the image, a 2x3 pixel pattern is used. The orientation of minutiae is measured in degrees. From the end of the ridge to the bifurcation of the ridge, the horizontal axis indicates zero degrees and increases in the counter-clockwise direction. The angle between the line projected at the ridge ending and the horizontal axis is the ridge ending's orientation. The angle between the line projected at the midpoint of the ridge bifurcation and the horizontal axis is known as bifurcation orientation.

There are two types of minutiae derived from a fingerprint: true and false minutiae. The quantity of wrongly identified minutiae is determined by the fingerprint's quality. Filtering these false minutiae is necessary to remove as many false minutiae as feasible without deleting true minutiae. Broken ridges, bridges, short ridges, and holes in the fingerprint are examples of repetitive minutiae. False minutiae like these can cause major issues when matching. Removing every bogus minutia one by one is time-consuming and difficult. As a result, the quality of each detail is calculated.

Types of Fingerprint Matching

The process of matching two fingerprint images is known as fingerprint matching. It's possible that the matching comes from the same person or from a different person. Genuine matching occurs when the matching comes from the same person, while imposter matching occurs when the matching comes from various people. Some of the fingerprint matching techniques include correlation-based matching, minutiae-based matching and ridge feature-based matching.

Correlation-based matching: In this method, for varied alignments, two fingerprint pictures are superimposed, and the correlation (at the intensity level) between corresponding pixels is determined. This approach shows promising outcomes in the authentication process when it comes to matching fingerprint patterns. With this strategy, high matching accuracy can be achieved. Gray-level information is collected, and fingerprints are matched using the correlation method.

Minutiae-based matching: The position and orientation of minutiae points obtained from a fingerprint are used in minutiae-based matching. This can be achieved with the help of algorithms such as the BOZORTH3 algorithm.

Ridge feature-based matching: Fingerprint matching might also be done using ridge feature maps. The use of both orientation and frequency information eliminates the need for fingerprint minutiae detection. In low-quality fingerprint photos, extracting minutiae is difficult, but other aspects of the fingerprint ridges pattern (local direction and frequency, ridge shape) may be recovered more accurately. This category includes methods for comparing fingerprints in terms of ridge pattern feature extraction.

Conclusion and Future Scope

With expenditures in development, research, and testing toward environmental sensors, the Automated Fingerprint Identification Systems (AFIS) business is predicted to create moderate income in the next years. However, the Automated Fingerprint Identification Systems (AFIS) market is maturing, and revenue for leading companies is projected to be small in the next years. The Automated Fingerprint Identification Systems (AFIS) market is expected to grow in the future due to factors such as rising demand for ASFI systems in the banking and finance, and government sectors, increasing advantages of automated fingerprint identification systems over traditional methods, and rising adoption of AFIS in smartphones and automated teller machines. However, a scarcity of experienced technicians is a key impediment to business expansion. Furthermore, factors such as expanding need for AFSI in border management and the global adoption of online transactions are likely to provide attractive prospects for market expansion.

References

https://www.innovatrics.com/glossary/afis-automated-fingerprint-identification-system/

https://en.wikipedia.org/wiki/Automated_fingerprint_identification

https://www.wallarm.com/what/automated-fingerprint-identification-system-afis

https://www.digitaljournal.com/pr/automated-fingerprint-identification-systems-afis-market-2022-global-industry-analysis-by-growth-key-players-share-revenue-trends-organizations-size-opportunities-and-regional-forecast-to-202

https://www.researchgate.net/publication/224226922_Implementation_and_evaluation_of_NIST_Biometric_Image_Software_for_fingerprint_recognition

https://www.alliedmarketresearch.com/automated-fingerprint-identification-system-market-A12196

https://global.chinadaily.com.cn/a/202105/06/WS60934c7aa31024ad0babc26e.html

https://www.digitaljournal.com/pr/global-biometrics-banking-market-2022-future-set-to-massive-growth-major-player-techshino-technology-state-grid-corporation-of-china-american-safety-council-inc