This example demonstrates an approach to create interactive applications for video processing. It shows the basic architecture for building model pipelines supporting model placement on different devices and simultaneous parallel or sequential execution using OpenVINO library in Python. In particular, this demo uses 3 models to build a pipeline able to detect faces on videos, their keypoints aka "landmarks" , and recognize persons using the provided faces database the gallery. The following pretrained models can be used:.
Betaface free online demo - Face recognition, Face search, Face analysis
Optimized for working in-camera with restricted CPU power it can also be used on-server, or in-cloud. IntelliVision facial recognition software searches an existing database of faces and compares them with the faces detected in the scene to find a match. Face Recognition detects faces in the camera's field of view - as many as 15 at the same time - and matches them against faces previously stored in the database. Anti-spoofing is provided through "liveness" testing without the need for a stereo or 3D camera. Faces can be enrolled in the database from existing still images or from the video camera itself.
See how you can benefit from a platform that takes care of deployment and DevOps and gets you up and running fast. Learn the tools to use for every step in the AI lifecycle. Clarifai's pre-trained Face Detection and Demographics Models can help get you started. Detect the presence of faces in images and videos.
Embed facial recognition into your apps for a seamless and highly secured user experience. No machine-learning expertise is required. Features include face detection that perceives facial features and attributes—such as a face mask, glasses, or facial hair—in an image, and identification of a person by a match to your private repository or via photo ID. Detect, identify, and analyze faces in images and videos.