FaceRecognizer

One of the most requested AI technologies by DAM customers is facial recognition. It offers clients a tremendous time savings, improves accuracy, and relieves staff of a lot of tedious work. Modula4 has developed FaceRecognizer, a facial recognition tool that can be adapted to work with virtually any DAM system. With a focus on privacy, accuracy, usability, predictable pricing, and multiple deployment options, FaceRecognizer is a best in class face recognition solution.

Unlike most facial recognition systems, FaceRecognizer doesn’t rely on a public cloud service. This provides much greater security and can satisfy risk assessment requirements for protecting PII (Personally Identifiable Information).

FaceRecognizer can be deployed in a variety of ways:

• Cloud hosted by the DAM vendor
• Cloud hosted by the client
• Cloud hosted by Modula4
• On-Premise connected to an on-premise DAM
• On-Premise connected to a cloud DAM

FaceRecognizer doesn’t require complex “pay per face” prices schemes. Public cloud services used by most other facial recognition tools for DAM charge based the quantity of images, the number of faces detected and analyzed, or other image processing steps. This makes estimating costs difficult. FaceRecognizer provides unlimited facial recognition for a fixed fee, making it predictable and more affordable for the client. And simplified pricing makes closing the sale easier for DAM vendors like you.

Commercial-grade facial recognition has greater requirements than consumer tools. Of course accuracy is critically important. Equally important is a comprehensive but easy to use interface. This improves productivity and the bottom line. Many facial recognition tools available for DAM systems are “checklist items” with limited functionality. FaceRecognizer’s user experience was created to be highly efficient. It’s designed and built as dedicated facial recognition from the ground up.

Highlights of FaceRecognizer User Experience:

• Multiple ways for training FaceRecognizer to identify faces
• Faces in newly ingested images are tagged automatically using machine learning
• Automatically finds matching faces in existing file stored in your DAM, prompting users for confirmation if needed
• A variety of tools and filters enable users to find untagged faces, or manually add faces and tags to the database
• Every interaction increases the accuracy of identifying faces
• Options to remove or ignore faces that are irrelevant for tagging or training the database
• Allows users to work an a subset of images and faces, as well as working on the entire database of faces as a whole.