Analysis of Face Recognition Vendor Test Results –

Our latest release of the ROC SDK v2.4 delivers another substantial set of improvements, demonstrated by its exceptional performance in the February 2nd, 2023 National Institute of Standards and Technology (NIST) Face Recognition Vendor Test (FRVT) Ongoing benchmark. 

While our organization has maintained a steady cadence of enhancements to our face recognition algorithm every 4 to 6 months since founding in 2015, the last 18 months show a clear surge in the pace of our improvements.

Labeled as “rankone-014” in the latest FRVT Ongoing report, the ROC SDK v2.4 face recognition algorithm outperformed hundreds of global competitors across numerous critical benchmarks, achieving: 

• #1 global leader in combined accuracy and efficiency
• #7 of 338 global solution providers in lowest Average Error Rate
• #1 of all key marketplace and U.S. competitors in Average Error Rate
• #6 of 338 global solution providers in lowest Visa Border dataset error rate
• #1 lowest Visa Border dataset error rate of all key marketplace and U.S. competitors

Specific performance metrics from ROC SDK v2.4 in the NIST FRVT Ongoing 2/2/23 report, labeled as “rankone-014” in Tables 8 to 29, are as as follows:

ROC SDK v2.4 Face Recognition Algorithm Accuracy and Efficiency Metrics

These error rates represent a 1.3x decrease as compared to our previous ROC SDK v2.3 (rankone-013 in FRVT). Accuracy rates are now consistently in excess of 99.5% accuracy / True Match Rates, while in many cases operating at False Match Rates of 1 in 1,000,000 (1E-6). 

As shown above, the Average Error Rate of ROC v2.4 now measures at 0.0061. This is simply computed from Tables 19 to 29 in the FRVT Ongoing report by measuring the arithmetic mean of all eight error rates liste

When comparing our Average Error Rate to other vendors listed, ROC.ai now ranks 7th of 338 global algorithm developers:

Face Recognition Average Error Rates: Global Top 50
Bar chart of the 50 global face recognition developers with the lowest Average Error Rate in FRVT Ongoing 2/2/2023. Rank One Computing is highlighted at 7th out of 338 total developers benchmarked.

When looking at key marketplace competitors, ROC.ai delivers the lowest average error rate: 

Face Recognition Average Error Rates: Market Competitors
Rank One Computing shown with the lowest average error rate out of ten market competitors in FRVT Ongoing 2/2/2023.

In addition to achieving best across-the-board accuracy of all key market competitors in FRVT Ongoing, ROC.ai also placed 6th globally out of 318 vendors in the Border dataset. When examining key competitors on the Visa Border dataset, ROC SDK easily had the lowest error rate:

Visa Border Face Recognition Error Rates: Market Competitors
Bar chart of ten market competitors with the lowest Visa Border error rate in FRVT Ongoing 2/2/2023. Rank One Computing is shown with the lowest error rate.

Ranking 7th globally in accuracy, the ROC SDK trails the global leader in each category by only only a fractions of a percentage points:

Comparison of ROC v2.4 vs. Top Global Vendor Per Category

While Chinese companies are leading 7 of the 8 categories, ROC SDK is often less than 0.1% away from the global leader in each category. 

Efficiency Analysis

While ROC.ai is a global leader in accuracy, we are also truly one-of-a-kind in combined accuracy and algorithmic efficiency. 

The four summary statistics for face recognition algorithm efficiency are provided in Tables 8 to 18 in the FRVT Ongoing report are: 

• Template size – the amount of bytes need to represent a face image
• Comparison time – the amount of time it takes to compare two templates and generate a threshold.
• Template generation time – the amount of time to process a face and produce a comparable template
• Binary size – the amount of memory / RAM needed by a device to run the algorithm

More information about these efficiency metrics can be found in our previous article, Procuring a Face Recognition Algorithm: Efficiency Considerations, as well as Hardware Considerations when Architecting a Face Recognition System.

For each of these four efficiency metrics, the NIST FRVT Ongoing report ranks each of the algorithms amongst all 478 algorithms (from 338 developers), with the best performing algorithm ranking #1. While the top ranks are often from highly inaccurate algorithms, ROC is unique in both ranking as both one of the most efficient algorithms and one of the most accurate algorithms. 

The ranking of each of ROC SDK’s algorithm efficiency metrics is as follows: 

ROC SDK v2.4 Face Recognition Algorithm Efficiency Metrics and Rankings

Out of the 478 algorithms, ROC SDK v2.4 has the 13th best average efficiency ranking. Our previous versions were also included in the benchmark test – ROC SDK v2.2 has the 12th best efficiency ranking, and ROC SDK v2.0 has the 7th best efficiency ranking). When examining the Average Error Rate across the top 20 most efficient algorithms, it is fairly stunning how much lower the error rate is with ROC compared to the other highly efficient algorithms: 

Face Recognition Efficiency and Error Rates: Global Top 20 by Efficiency Rank

ROC SDK provides error rates that are 2x to 50x lower than the other highly efficient algorithms. Only two of the top 20 most efficient vendors perform within 4x of ROC’s error rate, and most resulted in a more than 10x higher error rate.

When examining marketplace competitors, our efficiency ranking is substantially better than the top 10 competitors with lowest error rates: 

Efficiency and Error Rates: Market Competitors by Error Rate

ROC SDK combines the lowest error rate of all competitors, with unbeatable efficiency compared to  marketplace competitors. 

Finally, when examining the top 20 globals vendors with the lowest error rate, Rank One Computing emerges as being unparalleled in average efficiency ranking:

Efficiency and Error Rates: Global Top 20 Vendors by Error Rate

All together, ROC.ai is the unquestioned global leader in combined accuracy and efficiency.

FRVT 1:N Performance

In addition to ranking 7th globally in the FRVT Ongoing report, the ROC SDK v2.4 also achieved standout performance in the Feb 10th, 2023 NIST FRVT 1:N Identification report. The 1:N report measures the accuracy of searching large databases, which is an operational use-case with a long and historic legacy. 

Specific achievements of ROC.ai in the 2/10/2023 FRVT 1:N report include:

• Top-10 globally in Rank-1 hit-rate in all investigative search accuracy results (64K image dataset up to 12M image dataset)
• #8 of 388 algorithms in investigation frontal mugshot ranking 
• #6 of 250 algorithms in investigation mugshot webcam ranking 
• #9 of 277 algorithms in immigration visa border ranking 
• #5 of 222 algorithms in immigration visa kiosk ranking
• Rank-1 hit rate of 99.87% searching a database of 12M images

Demographic Biases in Face Recognition

Another key achievement of the ROC SDK v2.4 was further improvements in normalizing error rates and match thresholds across key demographic groups. While certain demographic plots for ROC SDK v2.4 / ‘rankone_014’ were not yet published in the 2/2/2023 FRVT Ongoing report, our organization still notably ranked 7th out of 406 algorithms in lowest False Match Rate (FMR) ratio between West Africa and Eastern Europe, according to the FRVT Demographic Effects in Face Recognition online leaderboard. We also ranked 7th globally for lowest False Non-Match Rate (FNMR) overall. 

Further examining the vendor scorecard for ROC SDK v2.4, the FNMR rates across a wide range of countries was quite stable: 

The differences in False Match Rate (FMR) between different countries and age groups was also highly stable, where the following results show the factor of difference in FMR at global threshold that corresponds to a FMR of 0.00003: 

These results demonstrate the stability of the ROC SDK across a wide range of demographic cohorts.

Face Recognition Accuracy Improvements Over Time

Finally, we will examine the improvement that ROC SDK has delivered over time to our partners and customers. 

From the FRVT scorecard, the following plot summarizes our error rate reductions over the last 5+ years: 

Over this time the FNMR has been reduced by over 100x. In just the last 18 months, ROC has released four new FR algorithms (in ROC SDK v1.26, v2.0, v2.2, and v2.4). Each of these releases has delivered an average error rate reduction on the NIST FRVT Ongoing benchmark between 1.3x and 1.4x. In total, this has results in a 3.4x error rate reduction over this period of time with roughly a 5x reduction on the Visa datasets: 

These improvements are not slowing down. ROC.ai is currently working on our next face recognition algorithm that will be released in the coming months. 

Summary

This comprehensive analysis of latest NIST results demonstrates a clear pattern of superior performance. ROC.ai dominates Western countries in face recognition accuracy and efficiency. We rank first in lowest Average Error Rate among all marketplace competitors, and in efficiency compared to all top-tier algorithms. In addition to being unparalleled in combined accuracy and efficiency, ROC.ai also delivers unparalleled customer support and straightforward business practices. Contracting mechanisms like Evergreen Licensing ensure that our customers and partners always have access to all the cutting-edge advancement being delivered by our R&D. 

Our differentiation does not end with accuracy, efficiency, and customer satisfaction. ROC.ai has established a rock-solid reputation as the most trustworthy developer of facial recognition algorithms in the world. We are employee-owned and financially-independent. Our technology is developed fully in-house in the U.S.A. We do not answer to the whims of investors who may have short-term goals that do not align with our customers, partners, employees. Instead, we are building a collaborative long-term roadmap to deliver faster, safer, more reliable computer vision solutions that can dutifully serve the greater needs of the world at-large.

The reasons are endless to begin integrating ROC SDK v2.4 face recognition algorithms into your organization. ROC.ai also offers top-tier algorithms for fingerprint detection, object detection, license plate recognition (LPR), optical character recognition (OCR), and more.

Reach out to team up with the world’s most trusted developer of face recognition solutions.

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ROC SDK V 2.4 Facial Recognition Algorithm Performance

• #1 Western algorithm
• #7 Global algorithm
• 2x improvement in error rates in just 5 months

Benchmark tests evaluated error rates for each algorithm across 8 data sets including Visa, Mugshot, Border Security, and ‘Wild’ or candid facial recognition. Across the board, our latest edition of ROC SDK v2.4 delivers massive improvements, reducing error rates by as much as 2x compared to ROC v2.2 released just 5 months ago.

“We are pleased but not surprised to see that our latest edition of ROC SDK leads the pack on facial recognition,” said ROC CEO Scott Swann. “Through continuous innovation, ROC will only continue to shape the future of the global biometrics landscape with objectively superior computer-vision capabilities.”

Face recognition technology can be used to enhance safety and security in a number of environments and scenarios, including:

1. Identity verification protects sensitive communications or transactions from fraud.
2. Access management protects sensitive facilities like banks, schools, government buildings, and data centers from intruders.
3. Video search streamlines criminal investigations by quickly identifying the most relevant segments of footage for human examination.

Following our recent rise to the top in fingerprint detection accuracy, these latest results further solidify the momentum we are building on the world stage. From fingerprint analysis to license plate detection and facial recognition, all ROC products are produced domestically in Denver, Colorado and Morgantown, WV, powered by the organization’s industry-leading proprietary software development kit (SDK).

Reach out now to start your journey toward integrating the best-in-class ROC AI facial recognition capabilities!

Rank One Computing (ROC) today announced its partnership with the Modular Open Source Identity Platform (MOSIP) to offer ROC’s industry leading biometric matching technology to MOSIP’s adopting nations. ROC’s multi-biometric SDK provides the only US-developed biometric matching algorithms that are top ranked by the National Institute of Standards and Technology (NIST). With this partnership, countries can leverage MOSIP’s proven, scalable platform to develop foundational identity (ID) systems with the ROC SDK’s top-rated face and fingerprint matching technologies to minimize fraud and deliver secure public and private services worldwide.

“Rank One Computing powers critical solutions for U.S. military, law enforcement and Fortune 500 companies and is now available to the entire world through MOSIP’s vendor-agnostic platform,” said David Ray, General Counsel and Chief Partnership and Privacy Officer. “We are excited to partner with MOSIP to provide customers worldwide with access to the best and most trusted identity technology the U.S. has to offer.”

Existing and future MOSIP-based large-scale identity projects, including biometric passports, voter registration and deduplication, border control, law enforcement, social services and a wide range of other applications can now use the ROC SDK to deploy secure, reliable person identification. The ROC SDK is a comprehensive, scalable software development kit that delivers single or multi-biometric identification through any operating system – Microsoft Windows, Linux, macOS, iOS, Android and ARM Linux.

“We believe in collaborating with our partners to make available a variety of cutting-edge technology solutions that work seamlessly with MOSIP,” said Sanjith Sundaram, Head – Biometric Ecosystem for MOSIP. “This helps adopting countries to achieve their goal of inclusion in a much more efficient and cost-effective way. We look forward to working with Rank One Computing and leveraging their expertise in the field in contribution to the common goal that we all share.” 

About ROC – Rank One Computing is a world leader in accurate and efficient face recognition (FR), fingerprint, and object recognition built on AI/ML computer vision.  We are the most trusted provider of AI/ML biometric matchers to the U.S. Military, Law Enforcement, Fintech, and Commercial organizations. We are employee-owned, ethics driven, and 100% Made in America. Our AI/ML computer vision algorithms lead the industry in security, accuracy, and speed as proven in NIST government testing, tactical military applications, and hundreds of millions of identity proofing transactions. Our company has offices in Denver, Colorado and Morgantown, West Virginia.

About MOSIP (Modular Open Source Identity Platform) – The International Institute of Information Technology, Bangalore (IIITB), a world-renowned technology university, is anchoring the MOSIP project as a global public good. The platform’s modular architecture, easy configuration, and customization abilities, enables countries the flexibility to build their foundational digital ID systems in a cost-effective manner. MOSIP is built on a strong bedrock of principles for security and privacy, and uses globally-accepted open standards. Funded by the Bill & Melinda Gates’ Foundation, Tata Trust, Omidyar Networks, NORAD, and Pratiksha Trust, the platform is being adopted by the Philippines, Morocco, and Togo, and piloted in Sri Lanka, Ethiopia, and Guinea.

Rank One Computing (ROC) shocks the identification community with the accuracy and performance of its first fingerprint algorithm submission to the National Institute of Standards and Technology (NIST) Proprietary Fingerprint Template (PFT) Test.

ROC accuracy was measured in the top three of all performers in every category, including these
impressive results:

• • • #1 most accurate performance on the challenging, cross-sensor, IARPA Nail-to-Nail evaluation sets
    • #1 most sensor-interoperable algorithm in the world
    • #1 most efficient algorithm submitted with search speeds soaring over 10,000x faster than all other participating vendors
    • #1 smallest template size – 160X smaller than the closest competitor.

With these breakout results and their new status as the only viable provider of 100% America-developed fingerprint algorithms, ROC now offers a compelling option for both
embedded and large-scale fingerprint system deployments to mitigate the risks from foreign developed AI/ML algorithms. In short, ROC now offers American-made algorithms that perform faster, require less computing power, and deliver more accurate results. As CEO B. Scott Swann states,

“ROC is extremely proud to put the U.S. on top of these AI/ML capabilities. For years there has been an over-dependence on foreign fingerprint technology. To my knowledge, the entire National Security screening apparatus has had no alternative to foreign fingerprint components to power these mission-critical systems. With this new algorithm, we are giving our customers the option to buy American while also deploying solutions that are more accurate and more efficient. As more agencies are migrating their enterprises to the cloud, these computing efficiencies translate to real cost savings.”

About ROC – Rank One Computing is a world leader in accurate and efficient face recognition and computer vision algorithms and the most trusted provider of Facial Recognition (FR) algorithms to the U.S. Military, Law Enforcement, Fintech, and Commercial organizations. We are employee-owned, ethics driven, and 100% Made in America. Our AI/ML computer vision algorithms lead the industry in security, accuracy, and speed as proven in NIST government testing, tactical military applications, and hundreds of millions of identity proofing transactions. ROC offers a growing suite of mobile and desktop computer vision solutions for all your identification and analytics needs. Our company has offices in Denver, Colorado and Morgantown, West Virginia.

Contact us today at bd@rankone.io to learn more, or visit us at www.roc.ai.

Rank One Computing’s (ROC) Facial LiveScan v2.0 is now available with real-time facial analytics, compliance checks with the ISO and ICAO standards, and anti-spoof detection based on ISO-certified liveness standards. Available as either a no-code, browser-based workflow or a native Android application, ROC Facial LiveScan 2.0 provides a flexible, scalable solution to developers of ID proofing, passenger kiosks, identification card credentialing, or time and attendance solutions to ensure that users are submitting facial imagery that complies with predefined standards that each organization can specify to best meet their mission needs.

Rank One Computing’s (ROC) Facial LiveScan 2.0 combines the latest in face recognition analytics, accuracy, and efficiency delivered in ROC SDK v2.3 with image quality validation and an updated LiveScan API that supports any system architectures. 

One key component of this upgrade is our recent ISO/IEC 30107-3 Level 1 certified facial liveness and anti-spoof method. Using a patented single-frame, passive liveness method, users can achieve 99% spoof detection accuracy. ROC Liveness significantly reduces identity fraud for unattended facial verification systems (e.g., ID proofing through mobile apps). 

Another critical component upgraded in this new Facial LiveScan version is the overhauled ROC Analytics framework. This capability in the ROC SDK now allows the real-time measurement of dozens of facial analytics. This includes ISO and ICAO standards compliance enabling secure government-issued document verification, travel processes, or enterprise know-your-customer (KYC) requirements. 

Finally, ROC is offering Facial LiveScan 2.0 as either a web-stack “no-code” browser-based enrollment workflow or a native Android application.  This means that ROC Facial LiveScan 2.0 will enable accurate, efficient, and configurable live scan capabilities to diverse use cases ranging from a mobile device, an embedded device such as an attended kiosk, or a thin-client browser application with a cloud-server back-end.

Contact us today at bd@rankone.io to learn more or visit us at www.roc.ai.

About ROC – Rank One Computing is a world leader in accurate and efficient face recognition (FR) and computer vision algorithms and the most trusted provider to the U.S. Military, Law Enforcement, Fintech, and Commercial organizations. We are employee-owned, ethics driven, and 100% Made in America. Our AI/ML computer vision algorithms lead the industry in security, accuracy, and speed as proven in NIST government testing, tactical military applications, and hundreds of millions of identity proofing transactions. ROC offers a growing suite of mobile and desktop computer vision solutions for all your identification and analytics needs. Our company has offices in Denver, Colorado and Morgantown, West Virginia.

Denver, CO, U.S.A. – Rank One Computing (ROC) has achieved Level 1 testing compliance for facial Presentation Attack Detection (PAD) standards in accordance with ISO/IEC 30107-­3 and tested by iBeta.

Unlike other facial liveness solutions on the market, ROC’s patented approach is entirely sensor and system agnostic, works with images and videos from any source, and returns a highly accurate liveness/anti-spoofing score in less than one second. As previously announced, ROC’s latest release of ROC SDK v2.3 significantly reduced the error rate of its patented, single-frame passive liveness solution by 2x to 5x across different spoof mediums and genuine user presentation categories as compared to its previous version. 

According to ROC CEO Scott Swann, “We are proud and excited to have iBeta’s testing compliance as another validation of our growing ID Proofing solutions. Across a range of different facial spoofing techniques, ROC is now delivering better than 99% PAD / spoof detection accuracy. We will continue pushing our AI/ML expertise to deliver better and better ID Proofing solutions to secure the growing number of commercial and government services that are shifting online.”  

ROC plans to submit its patented, single-frame liveness solution to the NIST FRVT PAD testing series, slated to begin in January 2023. The will continue to invest in improvements that provide users and partners with best-in-class solutions to conform with both ISO/IEC 30107-­3 facial PAD standards and ISO/IEC 29794-5 Face Image Quality standards that will be measured in the forthcoming NIST FRVT Specific Image Defect Detection (SIDD) benchmark. 

ROC’s approach is protected by US Patent US 10,839,251 B2 “Method and system for implementing image authentication for authenticating persons or items” since June 26, 2017. This foundational patent broadly covers any computer vision liveness approach that analyzes image regions to identify spatial relationships amongst pixels or groups of pixels.

Contact us to learn more!

• Liveness / Presentation Attack Detection (PAD)
• Tattoo Recognition
• Facial Analytics
• License Plate Detection

Liveness / Presentation Attack Detection (PAD)

Perhaps the most consequential improvement with v2.3 is a major reduction in error rates to ROC’s patented, single-frame, passive liveness algorithm. Rank One’s investment in liveness capabilities has yielded substantial improvements over the last year, building on the recent improvements in our previous v2.2 release.

The liveness solution currently ships with two different suggested operational thresholds: Security and Convenience. Convenience mode is primarily relevant to continuous authentication applications that need to be as frictionless as possible. Security mode is primarily relevant to single authentications related to sensitive systems and assets, such as identity proofing. 

The following improvements have been delivered to the ROC SDK v2.3 Liveness solution:

Liveness error rate comparison between ROC SDK v2.2 and v2.3

The provided error rates are measured on a large corpus of images that span all the Level A spoofing attacks as defined by the FIDO Alliance and in accordance with the ISO 30107-3 testing standard. The Genuine Reject Rate, which is referred to as the Bona Fide Presentation Classification Error Rate (BPCER) in the ISO standard, is when a genuine user’s presentation to a camera is incorrectly flagged as a potential spoof presentation. The Spoof Accept Rate, which is referred to as the Attack Presentation Classification Error Rate (APCER) in the ISO standard, is when a spoof presentation is incorrectly accepted as a genuine user presentation.

As shown in the above results, the ROC Liveness solution now provides the ability to severely limit spoof attacks while keeping the acceptance of genuine samples within the standards-compliant range. ROC will be eagerly submitting to the forthcoming NIST FRVT PAD benchmark. Through these improvements ROC will become certified as fully compliant with all applicable standards and industry best practices for facial biometric presentation attack detection.

Beyond single frame passive liveness, ROC further provides a suite of capabilities to support digital identity verification. This includes a wide range of facial analytics in support of ICAO and ISO standards. ROC offers easy to integrate “LiveScan” capabilities that allow ROC partners to easily capture a single, standards-compliant facial image for a user presenting themselves to a camera, with or without an active internet connection. 

Another round of enhancements to ROC Liveness will be on the way at the start of 2023. 

Tattoo Recognition

Rank One is a leading provider of tattoo recognition technology, with our algorithms deployed domestically and internationally within various law enforcement agencies. This capability is primarily used by law enforcement agencies with large databases of tattoo images captured at arrest booking to perform tasks such as identifying deceased victims who have little information available for identification aside from their tattoos. 

In the latest version of ROC’s tattoo algorithm, a major improvement enables the ability to accurately detect, localize, and represent tattoos images in a compact feature vector representation. Through the implementation of best practices in deeply convolved neural network representations of localized tattoo regions, ROC has now achieved drastically higher accuracy.

When evaluating 1:1 comparison accuracy for the ROC tattoo algorithm, which allows for easy generalization to the true 1:N use-case, the ROC tattoo algorithm achieves robust performance on this challenging problem: 

ROC v2.3 Tattoo Recognition Accuracy Results

Such recognition accuracies are quite powerful, especially when compared to the last published NIST Tatt-E report. While NIST Tatt-E is not currently accepting new submissions, accuracy comparisons of the top performing submission in the NIST Tatt-E report to ROC Tattoo v2.3 confirm that the ROC algorithm is significantly more accurate.

NIST Tatt-E primarily measured accuracy as Rank-10 retrieval rate on a gallery of 100,000 images. In this manner, the most accurate solution achieved a hit rate of 72.1% in that report. While not directly comparable, True Accept Rate at a False Accept Rate of 1 in 100,000 (0.001%) would serve as a lower bound for Rank-1 accuracy on a gallery of size 100k in cases where there is only one sample per identity.

By comparison, when the ROC algorithm is measured at a False Accept Rate of 0.001%, a True Accept Rate of 90.8% is achieved. Thus, increasing the challenge from being a Rank-10 match to a Rank-1 match, ROC (90.8%) still outperforms the industry leading solution (72.1%) by a wide margin. Such a clear separation in recognition accuracy demonstrates that the new ROC Tattoo algorithm is likely the most accurate solution in the world (and by a wide margin). 

Accuracy is not the only thing setting the ROC tattoo algorithm apart from participants in the NIST-C report. 

ROC v2.3 Tattoo Recognition Accuracy Metrics on a single a x64 CPU Core

As shown in the above table, the ROC Tattoo algorithm is incredibly fast and efficient. By comparison, in the NIST Tatt-E report the most accurate algorithm required 75.8 seconds to conduct a single search of a 100k image dataset. ROC can do this in less than 350 milliseconds. In other words, the ROC tattoo algorithm is not just the most accurate tattoo algorithm, it is also over 100x faster than the previously most accurate solution! Even the fastest solution submitted to NIST required 2.0 seconds to conduct the same search despite being far less accurate than other algorithms in that report.

In addition to the quantitative results of this new algorithm, the algorithm’s qualitative performance is arguably more impressive. While privacy reasons prevent us from showing operational tattoo images, top retrieval candidates generally have highly visual similarity to the probe candidate images. Such results make it immediately clear that this next-gen tattoo recognition capability will be a game-changer for forensic investigators in terms of the ability to find the same or highly similar tattoos to one in question. 

Facial Analytics

As opposed to face recognition which is purely based on the identity of a person contained in an image, facial analytics provide ancillary information regarding the presented face.  

Rank One has been an industry leader for many years in automated facial analytics, powering use-cases ranging from age verification, to LiveScan face acquisition for passenger travel, retail analytics, and many more. 

To address the rising demand for ROC Facial Analytics for deployment across a wide range of hardware architectures and software systems, the ROC SDK v2.3 delivers a complete overhaul of our analytics algorithms. All facial analytics can now be generated without first computing a facial recognition template. Through this change our full suite of facial analytics algorithms can be extracted in roughly 50ms total on a single CPU thread. These analytics include: 

• Age
• Gender
• Geographic Origin
• Emotion
• Facial pose
• Glasses
• Mask
• Eyes Visible
• Occlusion
• Facial Hair

In addition to the speed improvements from this new facial analytics method, it also delivers accuracy improvements to a majority of these different analytics methods. 

Finally, we added one new facial analytic feature to this release: the ability to remove the background from a facial photograph. In turn, the background of the photograph can be set to a consistent color in order to adhere to various compliance standards such as  ISO/IEC 19794-5 and ICAO 9303.

License Plate Detection

The final algorithmic improvement in ROC SDK v2.3 is a significant increase in accuracy for our license plate detection algorithm. This license plate detector is primarily used with ROC’s overall License Plate Recognition (LPR) solution.

Coming on the heels of the LPR improvements in v2.2, ROC is quickly developing broad capabilities in a range of use-cases for LPR technology.

—

Contact us today to learn more about ROC SDK v2.3! 

Denver, CO, U.S.A. – Rank One Computing (ROC) announced the latest improvements in their industry leading Software Development Kit (SDK) for face recognition and computer vision analytics! 

The ROC SDK version 2.3 brings a series of algorithm enhancements that benefit integration partners in a wide range of use-cases, including ID proofing, video analytics, and forensics. 

No improvement is more important than the new Presentation Attack Detection (PAD), or “facial liveness”, solution. With v2.3, ROC reduced the error rate of its patented, single-frame passive liveness method by 2x to 5x across different spoof mediums and genuine user presentation categories. In a range of scenarios, ROC is now measuring better than 99% PAD accuracy. These exciting improvements will better serve our partners who are developing identity proofing and access control systems. ROC expects to deliver even more improvements to liveness in early 2023.

In this release, ROC is also delivering a tremendous accuracy improvement to its tattoo recognition algorithm. The new algorithm deploys cutting edge advancements in convolutional neural networks and machine learning to reset industry expectations for tattoo recognition capability. When comparing ROC v2.3 Tattoo to the last published U.S. NIST Tattoo Recognition Technology Evaluation (Tatt-E) benchmark in 2018, the current ROC tattoo algorithm reduces error rates by an order of magnitude on similar law enforcement imagery when compared to the previous best reported algorithm. This radically improved solution effectively makes ROC the world’s most accurate tattoo recognition algorithm provider. As a result ROC SDK v2.3 will provide game-changing improvements to  forensic investigators working to identify deceased individuals and solve major cases. 

Another exciting feature is the overhauled version of ROC Facial Analytics. With v2.3 a new standalone algorithm can accurately estimate dozens of facial analytic features in less than 50 milliseconds on a single CPU core. Even at this speed, v2.3 still delivers mask detection, ISO/IEC 19794-5 and ICAO 9303 portrait quality compliance checks, facial pose estimation, age estimation, and a wide range of other analytics. Also new in this release, is the ability to help construct an ICAO compliant image by automatically cropping the face and removing the background.

Finally, as ROC continues to improve our license plate recognition capabilities, v2.3 delivers another round of enhancements to this solution with improved plate detection across a broad range of conditions. 

For a more detailed set of information about all of these algorithmic capabilities please see our recent technical article.

With each new release, the ROC SDK continues to raise the bar for software libraries and APIs that provide facial recognition and video analytics, all while maintaining lightning-fast speed and industry leading accuracy and efficiency. This is why ROC.ai is trusted by an ever-growing number of users and partners in Fintech, Security, Government, and other industries. 

Contact us today to learn more about ROC SDK v2.3 and other ways Rank One can support your organization!

About ROC: Rank One Computing (ROC) is the most trusted provider of Facial Recognition (FR) algorithms to the U.S. Military, Law Enforcement, Fintech, and Commercial organizations. We are employee-owned, ethics driven, and 100% Made in America. Our AI/ML computer vision algorithms lead the industry in security, accuracy, and speed as proven in NIST government testing, tactical military applications and hundreds of millions of identity proofing transactions. ROC offers a growing suite of mobile and desktop computer vision solutions for all your identification and analytics needs. Our company has offices in Denver, Colorado and Morgantown, West Virginia.

Press Release

Interested in meeting with our ROC stars? Well you’re in luck! The week of November 14, 2022 provides you with many opportunities to meet with us and check out our technology.

We will be participating in the following events:

ISC East – New York, NY – Nov 15-17

• David Ray, COO and General Counsel, discusses “Making the Biometric Upgrade: Enhancing Physical and Digital Security with Biometrics” on November 17 @ 1:00 PM
• Scott Swann, CEO, and Chris Centamore, SVP Strategic Marketing & Business Development will be meeting with clients and partners

WV Small Communities Big Success – Charleston, WV – Nov 15-17

ROC will showcase our tailored computer vision solutions for Safe Schools, FinTech, and Healthcare:

• ROC Watch – for live video alerting. Read more here.
• ROC SDK – the most trusted, accurate, and efficient AI/ML computer vision algorithm to recognize faces, vehicles, license plates, and more Read more here.
• ROC Enroll – for omni-channel Customer Enrollment, Visitor Management, & Identity Proofing

VP of Customer Success Blake Moore will highlight ROC’s latest and greatest during the sponsor spotlight on November 17 @ 9:50 am

You will also have the chance to meet with Jessica Sell, VP – Congressional Affairs & Community Outreach.

Indian Gaming Assoc. Mid-Year Conf – Fort McDonald, AZ – Nov 14-16

Meet with Eric Hess, VP – Sales and stop Table 12 to see:

• ROC Watch – for live video alerting. Read more here. 
• ROC SDK – the most trusted, accurate, and efficient AI/ML computer vision algorithm to recognize faces, vehicles, license plates, and more. Read more here. 
• ROC Enroll: Enroll customers remotely, build loyalty, and prevent fraud

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