Face-in-vivo detection method, device, equipment and storage medium

Abstract

An embodiment of the present application discloses a method, a device, equipment and a storage medium for detecting human face in vivo. The method comprises the following steps of: acquiring an RGB image and a depth image of a target object; calling a first neural network to process the RGB image to obtain a first processing result; invoking a second neural network to process the depth image to obtain a second processing result; according to the first processing result and the second processing result, determining whether or not the target object is a human face living body. An embodiment of the present application obtains an RGB image and a depth image of a target object, combining the two images to detect the face of the target object in vivo, the face texture clues and 3D face structureclues being combined to detect the face in vivo, which improves the accuracy and can effectively intercept the face paper, reproduce the face on the high-definition screen, synthesize the face video,face mask, 3D prosthetic model and other attacks. Moreover, it is simpler and more efficient to capture RGB images and depth images without user interaction.

Description

technical field [0001] The embodiments of the present application relate to the technical field of image recognition, and in particular, to a method, device, device, and storage medium for human face liveness detection. Background technique [0002] Face recognition technology has been widely used, but the face is very easy to copy by means of photos, videos, etc., so it is necessary to perform liveness detection on the face. [0003] In the related art, a human face liveness detection method based on an action command is provided. The terminal gives action commands in real time, such as blinking, shaking head left and right, nodding, opening mouth, etc., and the user performs corresponding actions according to the above action commands. The terminal usually gives multiple action commands in sequence. If all the action commands are executed correctly, it is considered to be a living face; otherwise, if any action command is executed incorrectly, it is considered not to be a...

AI Technical Summary

Problems solved by technology

[0004] The above-mentioned live face detection method based on action instructions requires the user to perform multiple actions in sequence according to the action instructions, and the efficiency of completing live face detection is low, and it has the ability to intercept attacks such as synthetic video, high-definition screen face recapture, 3D masks, etc. weaker

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