
Experience the epitome of speed and fairness with our face recognition model Top-ranked on NIST FRVT, coupled with an advanced iBeta level 2 liveness detection engine that effectively safeguards against printed photos, video replay, 3D masks, and deepfake threats, ensuring top-tier security.
This is on-premise SDK which means everything is processed on the browser and NO data leaves the device
npm install faceplugin-face-recognition-js
https://github.com/kby-ai/FaceRecognition-Javascript/assets/125717930/551b6964-0fef-4483-85a7-76792c0f3b56
Here are some useful documentation
Load detection model
loadDetectionModel()
Detect face in the image
detectFace(session, canvas_id)
Load landmark extraction model
loadLandmarkModel()
Extract face landmark in the image using detection result
predictLandmark(session, canvas_id, bbox)
Load liveness detection model
loadLivenessModel()
Detect face liveness in the image using detection result. (Anti-spoofing)
predictLiveness(session, canvas_id, bbox)
Load expression detection model
loadExpressionModel()
Detect face expression
predictExpression(session, canvas_id, bbox)
Load pose estimation model
loadPoseModel()
Predict facial pose
predictPose(session, canvas_id, bbox, question)
Load eye closeness model
loadEyeModel()
Predict eye closeness
predictEye(session, canvas_id, landmark)
Load gender detection model
loadGenderModel()
Predict gender using face image
predictGender(session, canvas_id, landmark)
Load age detection model
loadAgeModel()
Predict age using face image
predictAge(session, canvas_id, landmark)
Load feature extraction model
loadFeatureModel()
Extract face feature vector in 512 dimension
extractFeature(session, canvas_id, landmarks)
If you want to get better model, please contact us
$ claude mcp add FaceRecognition-LivenessDetection-Javascript \
-- python -m otcore.mcp_server <graph>