They argued that, despite massive improvements in deep-learning techniques, federal testing shows that most facial recognition algorithms perform poorly at identifying people besides White men.

As an essential step in facial recognition, 3D liveness detection works by leveraging algorithms and sensors to detect subtle noise in real and synthetic faces.

Demographic bias gaps are closing in face recognition, but how training images are sourced is becoming the field’s biggest privacy fight.

When that happens, the AI driving a facial recognition platform needs to rely on algorithms to fill in the missing details in a process called hallucination.

Police around the US say they're justified to run DNA-generated 3D models of faces through facial recognition tools to help crack cold cases. Everyone but the cops thinks that’s a bad idea.

The three primary uses of machine learning are face detection using Haar, object detection using the histograms of gradient orientation (HoG) and fingerprint recognition.