{"id":8264,"date":"2022-11-10T20:53:28","date_gmt":"2022-11-11T03:53:28","guid":{"rendered":"https:\/\/roc.ai\/?p=8264"},"modified":"2022-11-10T20:53:28","modified_gmt":"2022-11-11T03:53:28","slug":"roc-sdk-v2-3-2","status":"publish","type":"post","link":"https:\/\/roc.ai\/2022\/11\/10\/roc-sdk-v2-3-2\/","title":{"rendered":"ROC SDK v2.3 Delivers More Algorithm Improvements"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.19.2&#8243; collapsed=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.16&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text _builder_version=&#8221;4.21.0&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221;]<span style=\"font-weight: 400;\">ROC SDK version 2.3 continues to demonstrate the power of our AI\/ML computer development advancements with significant improvements to the following algorithms:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Liveness \/ Presentation Attack Detection (PAD)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><a class=\"inline-link\" href=\"https:\/\/roc.ai\/advanced-tattoo-recognition-solutions\/\">Tattoo Recognition<\/a><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Facial Analytics<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><a class=\"inline-link\" href=\"https:\/\/roc.ai\/automatic-license-plate-recognition-alpr\/\">License Plate Detection<\/a><\/span><\/li>\n<\/ul>\n<h2 id=\"liveness\"><span style=\"font-weight: 400;\">Liveness \/ Presentation Attack Detection (PAD)<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Perhaps the most consequential improvement with v2.3 is a major reduction in error rates to ROC\u2019s patented, single-frame, passive liveness algorithm. Rank One\u2019s investment in liveness capabilities has yielded substantial improvements over the last year, building on <\/span><a href=\"https:\/\/roc.ai\/2022\/08\/10\/rank-one-computing-ranks-1-in-latest-nist-frvt-for-combined-accuracy-and-efficiency\/\"><span style=\"font-weight: 400;\">the recent improvements in our previous v2.2 release.<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The following improvements have been delivered to the ROC SDK v2.3 Liveness solution:<\/span><\/p>\n<h6 style=\"text-align: center;\"><img decoding=\"async\" class=\"aligncenter size-full wp-image-8267\" src=\"https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/livenessv2-3.png\" alt=\"\" width=\"1600\" height=\"219\" srcset=\"https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/livenessv2-3.png 1600w, https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/livenessv2-3-1280x175.png 1280w, https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/livenessv2-3-980x134.png 980w, https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/livenessv2-3-480x66.png 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 1600px, 100vw\" \/><em>Liveness error rate comparison between ROC SDK v2.2 and v2.3<\/em><\/h6>\n<p><span style=\"font-weight: 400;\">The provided error rates are measured on a large corpus of images that span all the Level A spoofing attacks <\/span><a href=\"https:\/\/fidoalliance.org\/specs\/biometric\/requirements\/Biometrics-Requirements-v2.0-fd-20201006.pdf\"><span style=\"font-weight: 400;\">as defined<\/span><\/a><span style=\"font-weight: 400;\"> by the FIDO Alliance and in accordance with the <\/span><a href=\"https:\/\/www.iso.org\/standard\/67381.html\"><span style=\"font-weight: 400;\">ISO 30107-3<\/span><\/a><span style=\"font-weight: 400;\"> 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\u2019s 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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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 <\/span><a href=\"https:\/\/pages.nist.gov\/frvt\/html\/frvt_pad.html\"><span style=\"font-weight: 400;\">NIST FRVT PAD benchmark<\/span><\/a><span style=\"font-weight: 400;\">. Through these improvements ROC will become certified as fully compliant with all applicable standards and industry best practices for facial biometric presentation attack detection.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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 \u201cLiveScan\u201d 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.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Another round of enhancements to ROC Liveness will be on the way at the start of 2023.\u00a0<\/span><\/p>\n<h2 id=\"tattoo\"><span style=\"font-weight: 400;\">Tattoo Recognition<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the latest version of ROC\u2019s 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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When evaluating <\/span><a href=\"https:\/\/roc.ai\/2018\/11\/01\/face-recognition-dictionary\/#oneToOne\"><span style=\"font-weight: 400;\">1:1<\/span><\/a><span style=\"font-weight: 400;\"> comparison accuracy for the ROC tattoo algorithm, which allows for easy generalization to the true <\/span><a href=\"https:\/\/roc.ai\/2018\/11\/01\/face-recognition-dictionary\/#oneToN\"><span style=\"font-weight: 400;\">1:N<\/span><\/a><span style=\"font-weight: 400;\"> use-case, the ROC tattoo algorithm achieves robust performance on this challenging problem:\u00a0<\/span><\/p>\n<h6 style=\"text-align: center;\"><img decoding=\"async\" class=\"aligncenter wp-image-8266\" src=\"https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/tattooAccuracy-v2-3.png\" alt=\"\" width=\"285\" height=\"116\" \/><i><span style=\"font-weight: 400;\">ROC v2.3 Tattoo Recognition Accuracy Results<\/span><\/i><\/h6>\n<p><span style=\"font-weight: 400;\">Such recognition accuracies are quite powerful, especially when compared to <\/span><a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2018\/NIST.IR.8232.pdf\"><span style=\"font-weight: 400;\">the last published NIST Tatt-E report<\/span><\/a><span style=\"font-weight: 400;\">. 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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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 <\/span><i><span style=\"font-weight: 400;\">the new ROC Tattoo algorithm is likely the most accurate solution in the world<\/span><\/i><span style=\"font-weight: 400;\"> (and by a wide margin)<\/span><i><span style=\"font-weight: 400;\">.\u00a0<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">Accuracy is not the only thing setting the ROC tattoo algorithm apart from participants in the NIST-C report.\u00a0<\/span><\/p>\n<h6 style=\"text-align: center;\"><img decoding=\"async\" class=\"aligncenter wp-image-8265\" src=\"https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/tattooEfficiency-v2-3.png\" alt=\"\" width=\"375\" height=\"81\" \/><i><span style=\"font-weight: 400;\">ROC v2.3 Tattoo Recognition Accuracy Metrics on a single a x64 CPU Core<\/span><\/i><\/h6>\n<p><span style=\"font-weight: 400;\">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 <\/span><i><span style=\"font-weight: 400;\">over 100x faster <\/span><\/i><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In addition to the quantitative results of this new algorithm, the algorithm\u2019s 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.\u00a0<\/span><\/p>\n<h2 id=\"facialAnalytics\"><span style=\"font-weight: 400;\">Facial Analytics<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">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.\u00a0\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Age<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Gender<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Geographic Origin<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Emotion<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Facial pose<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Glasses<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Mask<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Eyes Visible<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Occlusion<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Facial Hair<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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\u00a0 ISO\/IEC 19794-5 and ICAO 9303.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">License Plate Detection<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">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\u2019s overall License Plate Recognition (LPR) solution.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u2014<\/span><\/p>\n<p><a href=\"https:\/\/roc.ai\/contact-us\/\"><span style=\"font-weight: 400;\">Contact us<\/span><\/a><span style=\"font-weight: 400;\"> today to learn more about ROC SDK v2.3!\u00a0<\/span>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>ROC SDK version 2.3 continues to demonstrate the power of [&hellip;]<\/p>\n","protected":false},"author":143642095,"featured_media":8291,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"<p><span style=\"font-weight: 400;\">ROC SDK version 2.3 continues to demonstrate the power of our AI\/ML computer development advancements with significant improvements to the following algorithms:\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Liveness \/ Presentation Attack Detection (PAD)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tattoo Recognition<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Facial Analytics<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">License Plate Detection<\/span><\/li><\/ul><h2><span style=\"font-weight: 400;\">Liveness \/ Presentation Attack Detection (PAD)<\/span><\/h2><p><span style=\"font-weight: 400;\">Perhaps the most consequential improvement with v2.3 is a major reduction in error rates to ROC\u2019s patented, single-frame, passive liveness algorithm. Rank One\u2019s investment in liveness capabilities has yielded substantial improvements over the last year, building on <\/span><a href=\"https:\/\/roc.ai\/2022\/08\/10\/rank-one-computing-ranks-1-in-latest-nist-frvt-for-combined-accuracy-and-efficiency\/\"><span style=\"font-weight: 400;\">the recent improvements in our previous v2.2 release.<\/span><\/a><\/p><p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">The following improvements have been delivered to the ROC SDK v2.3 Liveness solution:<\/span><\/p><h6 style=\"text-align: center;\"><img class=\"aligncenter size-full wp-image-8267\" src=\"https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/livenessv2-3.png\" alt=\"\" width=\"1600\" height=\"219\" \/><em>Liveness error rate comparison between ROC SDK v2.2 and v2.3<\/em><\/h6><p><span style=\"font-weight: 400;\">The provided error rates are measured on a large corpus of images that span all the Level A spoofing attacks <\/span><a href=\"https:\/\/fidoalliance.org\/specs\/biometric\/requirements\/Biometrics-Requirements-v2.0-fd-20201006.pdf\"><span style=\"font-weight: 400;\">as defined<\/span><\/a><span style=\"font-weight: 400;\"> by the FIDO Alliance and in accordance with the <\/span><a href=\"https:\/\/www.iso.org\/standard\/67381.html\"><span style=\"font-weight: 400;\">ISO 30107-3<\/span><\/a><span style=\"font-weight: 400;\"> 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\u2019s 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.<\/span><\/p><p><span style=\"font-weight: 400;\">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 <\/span><a href=\"https:\/\/pages.nist.gov\/frvt\/html\/frvt_pad.html\"><span style=\"font-weight: 400;\">NIST FRVT PAD benchmark<\/span><\/a><span style=\"font-weight: 400;\">. Through these improvements ROC will become certified as fully compliant with all applicable standards and industry best practices for facial biometric presentation attack detection.<\/span><\/p><p><span style=\"font-weight: 400;\">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 \u201cLiveScan\u201d 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.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Another round of enhancements to ROC Liveness will be on the way at the start of 2023.\u00a0<\/span><\/p><h2><span style=\"font-weight: 400;\">Tattoo Recognition<\/span><\/h2><p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">In the latest version of ROC\u2019s 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.<\/span><\/p><p><span style=\"font-weight: 400;\">When evaluating <\/span><a href=\"https:\/\/roc.ai\/2018\/11\/01\/face-recognition-dictionary\/#oneToOne\"><span style=\"font-weight: 400;\">1:1<\/span><\/a><span style=\"font-weight: 400;\"> comparison accuracy for the ROC tattoo algorithm, which allows for easy generalization to the true <\/span><a href=\"https:\/\/roc.ai\/2018\/11\/01\/face-recognition-dictionary\/#oneToN\"><span style=\"font-weight: 400;\">1:N<\/span><\/a><span style=\"font-weight: 400;\"> use-case, the ROC tattoo algorithm achieves robust performance on this challenging problem:\u00a0<\/span><\/p><h6 style=\"text-align: center;\"><img class=\"aligncenter wp-image-8266\" src=\"https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/tattooAccuracy-v2-3.png\" alt=\"\" width=\"285\" height=\"116\" \/><i><span style=\"font-weight: 400;\">ROC v2.3 Tattoo Recognition Accuracy Results<\/span><\/i><\/h6><p><span style=\"font-weight: 400;\">Such recognition accuracies are quite powerful, especially when compared to <\/span><a href=\"https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2018\/NIST.IR.8232.pdf\"><span style=\"font-weight: 400;\">the last published NIST Tatt-E report<\/span><\/a><span style=\"font-weight: 400;\">. 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.<\/span><\/p><p><span style=\"font-weight: 400;\">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.<\/span><\/p><p><span style=\"font-weight: 400;\">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 <\/span><i><span style=\"font-weight: 400;\">the new ROC Tattoo algorithm is likely the most accurate solution in the world<\/span><\/i><span style=\"font-weight: 400;\"> (and by a wide margin)<\/span><i><span style=\"font-weight: 400;\">.\u00a0<\/span><\/i><\/p><p><span style=\"font-weight: 400;\">Accuracy is not the only thing setting the ROC tattoo algorithm apart from participants in the NIST-C report.\u00a0<\/span><\/p><h6 style=\"text-align: center;\"><img class=\"aligncenter wp-image-8265\" src=\"https:\/\/roc.ai\/wp-content\/uploads\/2022\/11\/tattooEfficiency-v2-3.png\" alt=\"\" width=\"375\" height=\"81\" \/><i><span style=\"font-weight: 400;\">ROC v2.3 Tattoo Recognition Accuracy Metrics on a single a x64 CPU Core<\/span><\/i><\/h6><p><span style=\"font-weight: 400;\">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 <\/span><i><span style=\"font-weight: 400;\">over 100x faster <\/span><\/i><span style=\"font-weight: 400;\">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.<\/span><\/p><p><span style=\"font-weight: 400;\">In addition to the quantitative results of this new algorithm, the algorithm\u2019s 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.\u00a0<\/span><\/p><h2><span style=\"font-weight: 400;\">Facial Analytics<\/span><\/h2><p><span style=\"font-weight: 400;\">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.\u00a0\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">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:\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Age<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Gender<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Geographic Origin<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Emotion<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Facial pose<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Glasses<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Mask<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Eyes Visible<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Occlusion<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Facial Hair<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">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.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">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\u00a0 ISO\/IEC 19794-5 and ICAO 9303.<\/span><\/p><h2><span style=\"font-weight: 400;\">License Plate Detection<\/span><\/h2><p><span style=\"font-weight: 400;\">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\u2019s overall License Plate Recognition (LPR) solution.<\/span><\/p><p><span style=\"font-weight: 400;\">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.<\/span><\/p><p><span style=\"font-weight: 400;\">\u2014<\/span><\/p><p><a href=\"https:\/\/roc.ai\/contact-us\/\"><span style=\"font-weight: 400;\">Contact us<\/span><\/a><span style=\"font-weight: 400;\"> today to learn more about ROC SDK v2.3!\u00a0<\/span><\/p>","_et_gb_content_width":"","footnotes":""},"categories":[677108370,671645718],"tags":[],"class_list":["post-8264","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-roc-sdk"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Rank One Computing Enhances SDK v2.3 with Advanced 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