Neurotechnology Achieves Top Rankings in NIST Latent Fingerprint Recognition Test

Neurotechnology, a Lithuania-based biometric technology company, has achieved top rankings in the National Institute of Standards and Technology’s (NIST) Evaluation of Latent Friction Ridge Technology (ELFT). The company’s latent fingerprint algorithm demonstrated superior accuracy across multiple datasets and ranked among the leading performers in extraction speed, building on its previous strong performances in NIST evaluations.

The NIST ELFT evaluation used extensive datasets from the United States Department of Defense, FBI, and Michigan State Police, encompassing more than 1.6 million unique identifications. The assessment measured algorithms’ capabilities in correctly identifying latent prints and ranking them within the top 100 candidates, representing one of the most comprehensive evaluations of latent fingerprint technology to date.

Evaldas Borcovas, Biometrics Research Team Lead for Neurotechnology, stated, “We are thrilled to end this year with one more top-tier evaluation from NIST. ELFT represents the most advanced evaluation of fingerprint recognition for law enforcement applications.”

Founded in 1990 in Vilnius, Lithuania, Neurotechnology has established itself as a leader in deep learning-based solutions and biometric identification technologies. The company recently expanded its portfolio with the MegaMatcher ID platform, which combines face, fingerprint, and voice biometrics in a single development toolkit. The company’s products have been implemented in over 140 countries, supporting major initiatives including India’s Aadhaar program, which represents one of the world’s largest biometric identification systems, and general elections in Ghana and Liberia. The company’s systems currently process biometric data for approximately two billion people globally.

The ELFT evaluation serves as a benchmark for automated fingerprint identification systems, particularly those used in law enforcement and forensic applications. The assessment examines multiple aspects of latent fingerprint recognition, including identification accuracy, computational performance, and feature extraction algorithms. The evaluation is particularly significant as latent fingerprints, which are often incomplete or distorted prints left at crime scenes, present unique challenges for automated recognition systems.

Neurotechnology’s submission demonstrated superior performance in both accuracy across most datasets and single-finger and latent feature extraction speed. The evaluation represents a continuation of NIST’s previous Evaluation of Latent Fingerprint Technologies program, which operated from 2006 to 2012, and now includes expanded capabilities for analyzing various friction ridge types, including palm prints. This advancement is particularly relevant for law enforcement agencies seeking to modernize their biometric identification capabilities.

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December 10, 2024 – by the ID Tech Editorial Team