There is an old adage in systems engineering: garbage in, garbage out.  

No matter the source of the data used by an analytical system, if it is not captured in a manner conformant with how the algorithms and system were designed then it will not yield an ideal outcome. 

Across the vast range of facial recognition use-cases it is critical to prevent “garbage” from undermining the effectiveness of the system. Whether it is border screening applications, online selfie enrollment for banking and enterprise services, law enforcement use cases, or just about any other use of face recognition technology, there is a substantial benefit for preventing face images that are of insufficient quality from ever being processed. 

The way to prevent this “garbage” from being processed by a system is to flag bad images at the time of capture, which is the role of automated facial quality algorithms. Factors that may make an image lower quality include heavy blur, occlusions, low resolution, or extreme pose angles. 

Effective facial quality algorithms are so important to the deployment of face recognition systems that the National Institute of Standards and Technology (NIST) Face Recognition Vendor Test (FRVT) has a dedicated benchmark on “Face Image Quality Assessment”.

ROC delivers on quality

Rank One recently delivered to its customers a substantial improvement to its quality algorithm. This new quality algorithm as also submitted to NIST FRVT and the results are in: Rank One’s latest quality algorithm is #1 in the world! 

From the FRVT Quality website leaderboard Rank One is the best overall algorithm at filtering out low quality samples while maintaining a low False Non-Match Rate (FNMR) for the face recognition algorithm:  

Figure 1: Excerpt from the NIST Quality Website Leaderboard

Source: NIST FRVT Quality Summarization and Analysis

Per the NIST benchmark, by filtering out images with the lowest 1% in quality the FNMR is reduced from 0.01 to 0.0059. No other vendor is capable of achieving such a reduction in FNMR. 

This new quality algorithm, which is available in the latest version of the ROC SDK,  provides Rank One’s customers with a simple and effective method for rejecting non-conformant imagery from the system. In addition to this single automated quality metric, the ROC SDK also provides the ability to ensure facial imagery conforms with other specific image quality checks such as the ICAO standard

Rank One’s achievement in the NIST FRVT Quality benchmarked coupled with Rank One’s recent standout performance in the NIST FRVT 1:1 and 1:N benchmark further underscores what Rank One’s customer already know: there is not a more trusted provider of accurate, efficient, and easy-to-use face recognition technology than Rank One Computing.

About Rank One

Founded in 2015, ROC is an employee-owned company with headquarters in Denver, CO and offices in Morgantown, WV that develops its software entirely in-house and in the U.S.A. ROC delivers top performing face recognition and computer vision algorithms with a no-nonsense business approach and deep commitment to best practices in software engineering and pattern recognition algorithm design. We have initiated – and continue to lead – the charge to develop responsible AI by establishing the FR industry’s first code of ethics that governs our development and deployment of AI/ML algorithms and software in both commercial and government applications.

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