The most popular biometric authentication method now in use worldwide is reported to be the fingerprint-matching method. It has become an irrefutable parameter of identification because it is by far the most dependable and precise technique for recognizing a person.
Using a computer database of fingerprint information, the Automated Fingerprint Identification System (AFIS) is a biometric system that can distinguish between known and unknown fingerprints by searching and comparing them. Modern AFISs can search over a billion fingerprint records in a single second. The precision of the current algorithms is almost flawless. Law enforcement agencies often deploy automated fingerprint identification systems (AFIS) for criminal identification purposes, the most critical of which is 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 authenticate a user's stated identity, whereas identification systems determine to identify purely 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 most popular biometric authentication method now in use worldwide is reported to be the fingerprint-matching method. It has become an irrefutable parameter of identification because it is by far the most dependable and precise technique for recognizing a person. A biometric fingerprint reader records the impressions left by the patterns of ridges on a human's fingertips. A fingerprint is unique and only belongs to one individual. Consequently, it provides the safest and most dependable verification method by authenticating individuals and logging their imprints for future matching if needed.
Companies are constantly developing new fingerprint readers based on updated technology to make fingerprint recognition easier and more convenient. These USB fingerprint readers use optical sensors resistant to vibration, shock, and corrosion and can sense the touch of a finger in their vicinity. The innovative technology assures that it can capture a crisp and distortion-free image of a finger in any condition, whether scarred, damp, or even old.
Fingerprinting is being employed in a variety of scenarios. Airports, border security, banks, schools, colleges, and offices use fingerprinting these days. Many institutions, including hospitals, have begun to adopt fingerprint attendance. Hospitals have even included fingerprint matching as part of their access control procedures. Biometric fingerprint readers are the safest method for matching doctors', nurses', and other medical staff members' fingerprints to previously stored data so they can be admitted. These days, several hospitals even scan and examine their staff members' sub-thermal palm vein patterns. This ensures that precise identification is accomplished even if there are cuts or abrasions on the skin.
Architecture of Fingerprint Matching System
The following are the parts of the fingerprint identification system:
Enrolment Module:
This module is responsible for registering a user's fingerprint. The Fingerprint Scanner scans the impression of the finger and provides 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 their identity.
How Does Fingerprint Matching System Work?
In an automated fingerprint recognition system, fingerprint classification and matching are critical components. The fingerprint matcher compares features from the input search point to all appropriate records in the database to determine whether a plausible match exists. Numerous methods for automatic fingerprint matching have been proposed, such as image-based and minutiae-based methods. Minutia-based techniques are the most common, with practically every modern fingerprint identification and verification system using them.
The two main processes in fingerprint verification are minutiae matching and extraction. The first stage involves fingerprint sensing, traditionally accomplished by inking the finger and pressing it on a paper card, which is subsequently scanned, yielding a digital image.
This technique, still used in law enforcement applications, is known as offline acquisition. At the moment, fingerprint images can be obtained by pushing the finger against the flat surface of an electronic fingerprint sensor. The term "internet acquisition" describes this. During the pre-processing phase, the noise in the acquired image is removed, and the details are extracted from the pre-processed image. The last step in fingerprint matching is to provide the matcher with the fingerprint's minute patterns. Based on fingerprint matching, this matcher will generate a match score.
Minutiae Extraction
The basic fingerprint image comprises the bridges, valleys, cores, deltas, and pores. For minutiae-based matching, ridge ends, and bifurcations are employed to compare two fingerprints. It is customary to refer to a pixel with one neighbor as a ridge ending and a pixel with three neighbors as a ridge bifurcation. The ridge ending and ridge bifurcation, generally known as real minutiae, play a significant function 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 various 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 examined for white and black pixel assignment as part of this process. The binary picture is first scanned to determine ridge ends and ridge bifurcation. The scanning of patterns is done both horizontally and vertically. The image is scanned using a 2x3 pixel pattern. The orientation of minutiae is measured in degrees. The horizontal axis shows zero degrees at the end of the ridge and increases anticlockwise to the bifurcation of the ridge. 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.
Two types of minutiae are derived from a fingerprint: true and false. The fingerprint's quality determines the quantity of wrongly identified minutiae. 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
Fingerprint matching is the process of matching two images of a fingerprint. It's possible that the matching comes from the same person or 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 fingerprint-matching techniques include correlation, minutiae, and ridge feature-based matching.
Correlation-based matching:
In this technique, two fingerprint images are superimposed for different alignments, and the correlation (at the intensity level) between matching pixels is found. When it comes to matching fingerprint patterns, this method's authentication process yields promising results. 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. Using 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 ridge 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.
Patent Analysis
China has the greatest number of patents in fingerprint identification systems. One of the main reasons for this positive growth is the high adoption of biometrics solutions in the government and financial sectors. Still, a whole whack load of other areas are turning to biometrics. Furthermore, the European Patent Office announced that two Chinese innovators were finalists for the 2021 European Inventor Award for their combined hardware and software for real-time fingerprint verification. Yi and Pi Bo won attention for designing the first fingerprint sensor in the world that can concurrently check for a heartbeat, blood flow, and fingerprint pattern. Additionally, China is consistently seeking the adoption of technology that increases the precision with which persons are recognized using biometric systems, and they are also attempting to add Artificial Intelligence (AI) to improve the recognition of individuals.
Since 2012, the number of patent applications has grown, reaching a peak in 2017. Since then, the trend has been declining, but it can witness a positive rise in the near future. It is evident from the fact that the global biometric system market is expected to grow from USD 42.9 billion in 2022 to USD 82.9 billion by 2027, at a CAGR of 14.1%. The growing need for security and surveillance solutions in various application domains and increasing biometric technology breakthroughs across several industries are the main factors propelling market expansion.
Additionally, expanding biometric system use in the consumer electronics, BFSI, and automotive industries will increase demand for this technology in the near future.
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, finance, and government sectors, increasing advantages of automated fingerprint identification systems over traditional methods, and growing adoption of AFIS in smartphones and automated teller machines. However, a need for more experienced technicians is a key impediment to business expansion. Furthermore, factors such as the expanding need for AFSI in border management and the global adoption of online transactions will likely provide attractive prospects for market expansion.
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