Enrollment is the process of detecting faces in an image (or video frame) and creating "templates" that encode the identifying characteristics of each face. The faster the enrollment speed, the less computing power required to support a face recognition application. If your face recognition application requires even faster enrollment speeds, please inquire about our ROC Embedded algorithm. Amongst the top 100 algorithms submitted to NIST FRVT Ongoing, the ROC SDK is typically 7x faster.
The comparison speed is how long it takes to measure the similarity between two templates. The faster the comparison speed, the faster the verification or search results. Amongst the top 100 algorithms submitted to NIST FRVT Ongoing, the ROC SDK is typically 13x faster.
Speed is measured on Intel Xeon CPU E5-2630 v4 @ 2.20GHz using a single thread. Source: NIST FRVT Ongoing.
The template size is the amount of storage space required to save a template extracted in the enrollment process. The ROC SDK's small template size enables us to search large databases on a smartphone FASTER than some of our competitors can on a server.
Memory footprint is the total hard disk space that is required to store the ROC SDK software libraries. At 155MB, the standard ROC SDK memory footprint is one of the smallest on the market. At 7MB, the ROC Embedded memory footprint opens the door for mobile applications with powerful facial recognition capabilities embedded into the app and running on the edge device, without a data connection.
The ROC SDK is a fully native solution, with minimal system requirements and no internet is connection required. The ROC SDK supports all major operating systems and computer architectures. The ROC SDK exposes a C API with wrappers for Python, Java, C# and Go. A robust command line interface even allows constructing systems from shell scripts.
Perform 1:1 verification with industry leading accuracy.
Search millions of faces on a mobile device, or billions on a server, in seconds.
Locate faces in photos and video with industry leading detection speed.
Track eye and chin locations.
Group unlabeled faces by identity.
Predict a face likelihood to generate a successful match.
Analyze a face to predict its age, emotion, ethnicity and gender.
Process videos in real-time using a single core on a mobile device or server.
Determine where a person is looking relative to the camera using roll, yaw and pitch measurements.
Detect print and digital screen replay attacks using a single image.
Count persons in a video stream seamlessly.
Detect eyeglasses, sunglasses, or no glasses.
This article will equip you with the knowledge to assess the efficiency requirements of your face recognition system. In turn, you will be able to factor this important consideration into your procurement process and potentially eliminate certain algorithms before the time consuming step of performing internal evaluations.
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