Automated Fingerprint Identification System (AFIS)

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.

Architecture of Fingerprint Matching System

  1. 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.
  2. Database Module: The Database Module stores the User templates. The Processing Module’s Feature Vector is evaluated against one or more existing templates.
  3. 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.

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.

  1. 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.
  2. 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.

Patent Analysis

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

  1. https://www.innovatrics.com/glossary/afis-automated-fingerprint-identification-system/
  2. https://en.wikipedia.org/wiki/Automated_fingerprint_identification
  3. https://www.wallarm.com/what/automated-fingerprint-identification-system-afis
  4. 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
  5. https://www.researchgate.net/publication/224226922_Implementation_and_evaluation_of_NIST_Biometric_Image_Software_for_fingerprint_recognition
  6. https://www.alliedmarketresearch.com/automated-fingerprint-identification-system-market-A12196
  7. https://global.chinadaily.com.cn/a/202105/06/WS60934c7aa31024ad0babc26e.html
  8. 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

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