Face detection and recognition method, device and system

Abstract

The invention discloses a face detection and recognition method, device and system. The face detection and recognition method includes: detecting a face area from an acquired two-dimensional image; combining the two-dimensional image data of the face area with the acquired Project the depth image data into the same coordinate system to obtain three-dimensional image data; use the three-dimensional image data to perform overall concave-convex curvature detection and multiple local concave-convex curvature detections, and when the overall concave-convex curvature is within a first predetermined range , determining whether the local weighted concave-convex curvature obtained from a plurality of local concave-convex curvatures is within a second predetermined range, and if the local weighted concave-convex curvature is within the second predetermined range, then it is determined that the face area is real face. The present invention does not require the customer to do additional cooperation during the entire detection and recognition process, and can ensure a high detection and recognition rate under the premise of a high recall rate, and avoid detecting and recognizing a picture face as a real human face.

Description

technical field [0001] The present invention relates to the field of artificial intelligence, in particular to a face detection and recognition method, device and system. Background technique [0002] Face detection and recognition is a biological detection and recognition technology for identity detection and recognition based on human facial feature information. Use a camera or camera to collect images or video streams containing faces, and automatically detect and track faces in the images, and then perform a series of related technologies on the detected faces, usually also called portrait detection and recognition, face detection identify. [0003] Because of the direct and convenient features of face detection and recognition, users do not have any psychological barriers, and are easy to be accepted by users, so they have been widely researched and applied. [0004] Traditional face detection and recognition is based on 2D face detection and recognition cameras and a...

AI Technical Summary

Problems solved by technology

However, for computers, the depth information obtained through the two cameras is not particularly accurate, because it will be affected by light and other factors, and the error is relatively large; in addition, the performance requirements of the computing unit are very high, which makes the existing binocular The productization and miniaturization of the system are more difficult

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