Neurotechnology, a pioneering force in deep learning-based biometrics, has just announced a groundbreaking achievement: its latest fingerprint algorithm has secured the top position in the rigorous National Institute of Standards and Technology (NIST) Friction Ridge Image and Features (FRIF) Technology Evaluation Exemplar One-to-Many (FRIF TE E1N). This dominant performance, achieved across the majority of the evaluation’s diverse testing categories, underscores the company’s superior accuracy and sets a new benchmark for the industry.
The FRIF TE E1N is a crucial assessment, specifically designed to scrutinize the core template creation and search algorithms that form the bedrock of Automated Biometric Identification Systems (ABIS). Its comprehensive scope spans everything from single-finger identification to complex, large-scale database searches, making Neurotechnology’s triumph particularly significant.
Unrivaled Performance Across Rigorous Categories
Neurotechnology’s algorithm showcased unparalleled robustness and precision across all three distinct testing environments within the FRIF TE E1N evaluation:
Class A: Single and Two-Finger Identification
In Class A, which involved plain impressions of left and right index fingers tested individually and in two-finger combinations, Neurotechnology’s algorithm delivered the best performance among all participants. This top-tier accuracy confirms its exceptional capability to identify subjects even when limited to single index finger records, proving vital for rapid identification needs in various applications.
Class B: Multi-Finger Slap Impressions
The second category, Class B, focused on Identification Flat captures (slaps) in a 4-4-2 configuration, encompassing right, left slap, and thumbs against a full ten-finger enrollment database. Here, the algorithm demonstrated breathtaking precision in identifying simultaneous multi-finger impressions. Achieving a nearly perfect score with zero errors in the majority of experiments, this performance highlights its steadfast reliability for critical field operations and high-volume identity verification.
Class C: Comprehensive Ten-Finger Scenarios
Class C, the most demanding category, utilized plain impressions of all ten fingers in a 4-4-1-1 configuration. Neurotechnology’s submission once again delivered top-tier performance, unequivocally confirming the algorithm’s robustness and its profound suitability for the most stringent law enforcement and national security scenarios.
A New Era in Biometric Accuracy
“This same evaluation was last conducted in 2012, and while many tenders have relied on that old evaluation when selecting technologies, these results shift the technology leaderboard and reflect the current state-of-the-art technology,” stated Evaldas Borcovas, Head of Biometrics Research for Neurotechnology. He continued, emphasizing the significance: “Reaching the highest accuracy in the NIST FRIF TE E1N evaluation – and achieving zero-error rates in some experiments – reaffirms that our biometric technologies are suitable for the most demanding applications, such as law enforcement and national-scale identity programs.”
This latest triumph extends Neurotechnology’s formidable legacy of developing highly precise and accurate fingerprint algorithms. The company has a consistent track record of leading NIST technology evaluations, including the Proprietary Fingerprint Template III (PFT III), Evaluation of Latent Fingerprint Technology (ELFT), Minutiae Interoperability Exchange (MINEX) III, and Slap Fingerprint Segmentation Evaluation (SlapSeg) III.
Further cementing its leadership, Neurotechnology also recently secured first place in UIDAI’s Biometrics SDK Benchmarking Challenge for the fingerprint modality, underscoring its continuous innovation and excellence in the biometrics arena.