The Automated Fingerprint Identification System (AFIS) is a biometric identification (ID) methodology that uses digital imaging technology to obtain, store, and analyze fingerprint data.  Automated fingerprint identification is the process of using a computer to match fingerprints against a database of known and unknown prints. AFIS is primarily used by law enforcement agencies for criminal identification purposes, the most important of which is the identification of a person suspected of committing a crime or linking a suspect to other unsolved crimes. [1]

History and Development

At first glance, the principle of using modern technology to automate the laborious and time-consuming task of manually processing fingerprints taken from a suspect and/or crime scene appears straightforward. However, the evolution of the AFIS into a highly efficient and effective tool, capable of scrutinizing vast databases and providing potential fingerprint matches in a matter of minutes, is the product of intensive research and development that now stretches back over more than five decades.

Biometric identification is based on the principle that each individual can have a set of recognizable and verifiable data, which are unique and specific to them. For fingerprints, according to Sir Francis Galton, the probability of finding two similar fingerprints is one in 64 billion even with twins.

Criminal identification systems originally emerged in the late 19th century. They were triggered by the landmark development of the Henry System of fingerprint classification in which fingerprints are sorted by physiological characteristics and anthropometrics also known as Bertillon system, in which measurements are obtained from suspects and filed.

In the UK, the Metropolitan Police started the use of biometrics for identification in 1901. In the US, it was initiated by the New York police in 1902 with French police initiating the same process in late 1902.

By the 1920s, the FBI had created its first Identification Department, establishing a central repository of criminal identification data for US law enforcement agencies.

By 1999, 500 AFISes were deployed around the world. Today, according to a 2017 study from Markets and Markets, the automated fingerprint identification system market size is estimated to reach USD 8.49 Billion by 2020, at an estimated CAGR of 21.0% between 2015 and 2020.


Established in 1999, IAFIS stands for Integrated Automated Fingerprint Identification System, is the name of the FBI AFIS now upgraded to the Next Generation Identification (NGI) is the world’s largest collection of criminal history. Maintained by the FBI Criminal Justice Information Service, it contains the fingerprints of more than 143 million criminal and civil individuals at the end of February 2019 according to the FBI monthly fact sheet. [2]

Applications of AFIS

  1. Corpse Identification
  2. Criminal Investigation
  3. National ID
  4. Access Control
  5. Banking Security
  6. Passport Control (INSPASS)
  7. Voting
  8. Identification of missing children.
  9. Cellular Phone, etc.[3]


Steps involved in AFIS

  1. Fingerprint Capture/Sensing
  2. Fingerprint Recognition/Representation
  3. Minutiae Feature Extraction
  • Orientation Estimation
  • Segmentation
  • Ridge Detection
  • Minutiae Detection
  • Post Processing
  1. Fingerprint Classification
  2. Fingerprint Matching
  3. Match Verification.


Conclusion and Future Aspects

Fingerprint-based personal identification is an important biometric technique with many current and emerging applications. This article has provided an overview of fingerprint based personal identification including history and development, its applications and working process.

There is a popular misconception that automatic fingerprint matching is a fully solved problem because it was one of the first applications of automatic pattern recognition. Despite notions to the contrary, there are a number of challenges that remain to be overcome in designing a completely automatic and reliable fingerprint matcher, especially when images are of poor quality as in the case of latent prints. Although automatic systems are successful, the level of sophistication of automatic systems in matching fingerprints today cannot rival that of a dedicated, well-trained fingerprint expert. Still, automatic fingerprint matching systems offer a reliable, rapid, consistent, and cost-effective solution in a number of traditional and newly emerging applications. The performance of various stages of an identification system, including feature extraction, classification, and minutiae matching, do not degrade gracefully with deterioration in the quality of the fingerprints. Most of these deficiencies in the existing automatic identification systems are overcome by having an expert interact with the system to compensate for the intermediate errors.



  1. Automated Fingerprint Identification, [Online] ( Accessed on 28/06/2019.
  2. Gemalto, (2019) “Automated Fingerprint Identification System- a short history” [Online] ( Accessed on 28-06-2019.
  3. Banshpal, R. (2013) “Fingerprint Recognition” [Online] ( Accessed on 28-06-2019.