This model detects all faces in a given image and creates an embedding—a biometric representation—for each detected face. Embeddings can be added to a database (i.e. enrollment) or matched against a database of registered embeddings to perform verification or identification functions. The key functions provided include:
Face detection: Detecting the presence and location of all faces in a submitted image. At this stage, key locations on the face (“Landmarks”) will be noted, and a quality score will be calculated to determine if the image of a face under consideration is sufficiently high quality to ensure a high level of face recognition matching accuracy.
Embedding generation: Computation of a set of unique mathematical vectors (“Embeddings”) for every face.
Face enrollment: Adding an embedding to a database when a face is presented for registration.
Face matching: Comparing the embedding for a given face against an enrollment database.
Many models are available for limited use in the free Modzy Basic account.
Paravision face recognition has been extensively benchmarked by the National Institute of Standards and Technology (NIST) Face Recognition Vendor Tests (FRVT), which are the gold-standard global benchmarks for face recognition testing. These tests include very large, statistically significant data sets testing a variety of conditions, from Visa (travel document) quality to fully unconstrained. Paravision has performed exceedingly well across all tests, including 1:1 Verification, 1:N Identification, and Image Quality Metrics. Paravision is ranked #1 in the U.S., UK, and Europe on all major NIST FRVT leaderboards, including 1:1, 1:N, and face mask effects. Paravision has demonstrated superior accuracy in the most challenging benchmarks, delivering #3 global performance 1:N identification, #2 global performance in face mask effects, and #1 global performance in profile (90 degree) matching.
In order to deliver the demonstrated level of accuracy, Paravision has developed a highly sophisticated machine learning infrastructure, combining very large and diverse datasets with the latest generation AI technologies. In addition to training, Paravision has also developed a highly robust set of internal benchmarks that assess performance across imaging conditions as well as subject demographics such as age, gender, and ethnicity. By pairing best-of-breed ML and biometric performance benchmarking, Paravision has shown an ability to consistently perform at a world-class level and continuously improve even over short release intervals.
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