Neurotechnology reigns atop NIST benchmark for latent fingerprint algorithms

Neurotechnology’s latent fingerprint algorithm has topped NIST’s Evaluation of Latent Friction Ridge Technology (ELFT) benchmark for accuracy.

The evaluation by the National Institute of Standards and Technology tests how well a provider’s algorithm can identify the right person using latent prints. It references datasets from major U.S. government and law enforcement institutions – a core market for Neurotechnology.

A release says the Vilnius-based firm’s ELFT submission “showcased its latent fingerprint algorithm as the most accurate across most datasets and among best in extraction speed.”

“We are thrilled to end this year with one more top-tier evaluation from NIST,” says Evaldas Borcovas, biometrics research team lead for Neurotechnology. “ELFT represents the most advanced evaluation of fingerprint recognition for law enforcement applications. Our biometrics research team takes pride in this remarkable achievement, which highlights our unwavering commitment to advancing our technology and establishing new quality benchmarks in biometric recognition.”

Neurotechnology, which was founded in 1990, also made a recent appearance on NIST’s age estimation evaluation by demographic group for men and women born in six regions of the world.

Idemia, Thales join Neurotechnology in ELFT top 10

In addition to Neurotechnology, the NIST ELFT leaderboard top ten for the FBI-Provided Solved Data Set # 1: FNIR at FPIR = 0.01 features algorithms from Idemia, Innovatrics, Beijing Hisign Technology Co., Ltd., Thales and NEC. Other names in the top 20 include ROC, Dermalog, and Brazilian companies Griaule and Antheus Technology Ltd.

For extraction speed, ROC ranks fastest, followed by Thales, Idemia, Beijing Hisign Technology Co., Dermalog, Griaule and Neurotechnology.

Article Topics

biometric identification  |  biometric testing  |  biometrics  |  digital identity  |  ELFT  |  fingerprint biometrics  |  Neurotechnology