Face Recognition Software

A face template is the numerical encoding of a face image that contains uniquely identifying characteristics and allows computers to differentiate between people. They can be generated from a static image or series of video frames. Uniquely identifying characteristics associated with the face image are extracted and represented by a numerical encoding which cannot be reverse engineered to mitigate identity spoofing. Each Face Recognition software vendor has a unique approach for this encoding process. File size and matching speed are critical differentiators in addition to accuracy.

The ROC SDK is available for Windows, Mac OS, iOS, Android, and Linux. The ROC SDK supports both x86-64 and ARM processors. For older processors we provide a legacy build for CPUs that do not have the FMA instruction set.

Both! The ROC SDK performs accurate face matching on-edge and on large-scale cloud applications, given our small file size. And we do it without compromising performance, so we can work equally as well on large enterprise applications.

ROC.ai currently supports a broad range of use cases across both the public and private sectors. We work heavily in the commercial space, especially FinTech, and power ~50% of the ID-proofing community, including various online banking, eKYC, and anti-fraud solutions. Within the public sector, we power over a dozen law enforcement agencies and several large federal customers. We are also proud to work with several organizations addressing social issues like human trafficking, child exploitation, counterterrorism, and election participation.

The sky is the limit! We understand that each of our partners have different requirements, and we have developed our Face Recognition capabilities with this in mind. Our products support galleries in the order of millions. Currently, our largest deployment supports a federal government organization with a 500M template gallery.

Yes. In response to COVID, ROC.ai applied its extensive data collection and research capabilities on the challenge of uniquely recognizing people wearing face masks using only the eye/eyebrow region of the face.

Yes, ROC.ai is very accurate when used on children. However, there are important nuances to this answer.

1. Rapid physiological development – Early adolescence is a time in physiological development when the face and cranium experience major changes.
2. Availability of data – Face Recognition algorithms exhibit high performance on population segments on which they have been trained. Training data for minors is not as readily available as data for adults.
3. The time span between enrollment and subsequent matching is especially important to consider when Face Recognition systems are used with younger populations. Overall, we estimate that Face Recognition works well on people over the age of 11.

We use a single image/single frame and have several models looking for spoofs on laptops, glossy photos, printer paper and phones. ROC has a US patent for its proprietary passive biometric liveness detection. The method uses micro-texture analysis to confirm the authenticity of a biometric image during the matching process. ROC's liveness detection does not require the use of any specialized hardware, and is able to perform the liveness check using only the standard RGB cameras found in smartphones, security cameras, and other devices. It also does so using only a single image or video frame, which minimizes the amount of processing bandwidth needed to carry out the check.