Facial liveness API has evolved to combat fraud and ensure the integrity of facial biometrics used for identification and identity verification. Face recognition can accurately answer the question, "Is this the right person?" "Is this a real person?" This is where the passive liveness detection API comes into role.
Facial liveness detection combines facial recognition with facial liveness detection to identify whether a biometric sample is being taken from an alive subject present at the time of collection. As a result, it prevents fraudsters from spoofing a facial recognition system via presentation attacks.
Check if the selfie your users take is live and correctly clicked to keep your records. Face liveness detection aids in identifying fraud by ensuring that the image provided is neither a picture of a photograph, a passport-sized image, or an image of another person on a cell phone/laptop screen.
The leading identity and access management vendors, onboarding and KYC providers, access control firms, IoT developers, and enterprise customers all around the world are switching to passive liveness.
liveness API is highly accurate
Verification only requires the selfie.
TAT of the API is extremely low, allowing real-time authentication
API structure is precise for quick integrations
Automate workflows
Automate the process of determining whether or not an image is active.Tackle fraud at the source
During the onboarding process, remove anyone who appears to be fake.Eliminate manual error
Allow a machine-based algorithm to do the work for you instead of doing it yourself!
As input, send us a selfie, and we'll analyse the following data points for you.
Is the face real? Is the photo cropped?
Is it possible that multiple faces have been detected?
Is the photograph well-lit?
Is the face too close or too far away from the camera?
Don't deploy face recognition for authentication without passive facial liveness. Face API can integrate passive liveness detection features with any face recognition application to prevent presentation attacks.
Improved security and fraud detection are two of the advantages of liveness detection. Passive face liveness has the advantage of being secure and simple to utilise. Unlike other products, MxFace API's liveness detection is completely invisible to the end-user, who doesn't even know it's taking place behind the scenes. As a result, your programme does not provide any information that fraudsters can use to circumvent the system. Passive living reduces abandonment and the need for human intervention because it is less confusing for users. Take a look at the demo for yourself.
The angle of Out-of-plane rotation (face pitch and yaw) must be from -30±3 to 30±3 degrees.
The angle of in-plane rotation (face roll) is not restricted. The face detection algorithm can detect a face in any rotation in a roll and adjust for it automatically. Avoid photos with a face roll angle greater than 45.
The minimal distance between the eyes (interpupillary distance) of the subject: is 60-80 px.
For mobile platforms, the minimum face size (depending on the pipeline) is 192-224 pixels in any dimension, and the face should take up at least 25% of the image. The minimum face size for desktop and webcam photographs is 350 pixels, and the face should take up at least 15% of the image.
Image must have only one face.
Face must be fully visible without any blockage.
The faces should have enough padding (3-25 pixels) around it.
Avoid wearing sunglasses.