Human face living body detection method and equipment

Abstract

The invention relates to a human face living body detection method and device, and the method comprises the following steps: S1, receiving a depth image, a color image, and an infrared image which comprise a human face region, and carrying out the registration; S2, detecting human face areas in the depth image, the color image and the infrared image; S3, preprocessing the face region depth image to realize image enhancement and extract an effective face depth image; And S4, respectively inputting the effective face depth image, the color image of the face region and the infrared image into thetrained cascade depth convolutional neural network, and carrying out accurate face living body detection. The device comprises a computer program for realizing the method. Through multi-source information fusion and the cascade deep convolutional neural network, the problem that a traditional monocular color camera cannot well recover face three-dimensional information is solved, and the accuracyof face recognition is improved.

Description

technical field [0001] The invention relates to a human face living body detection method and equipment. Background technique [0002] Face liveness detection technology is a cutting-edge technology in the field of artificial intelligence. This technology is based on a new type of 3D sensor and uses deep learning technology as a development framework. In the field of face recognition, real faces and fake faces Make effective judgments to resist the attack of illegal and false faces on the face recognition system, and provide a more secure application environment for applications with face recognition. [0003] Static face liveness detection is a technology to detect whether the face area contained in the depth image is a real face collected by the current device. Output 1 if the detection result is true, and output 0 if false. The premise of realizing face liveness detection is to build a face liveness detector. Generally speaking, existing technologies construct face det...

AI Technical Summary

Problems solved by technology

This technology needs to calibrate the internal parameters of the camera, and the accuracy of the calibration of the internal parameters of the camera, the rotation and displacement relationship between the cameras that take different images, and the quality of the image have a great impact on the reconstructed face depth value, so This method has a high false detection rate for live detection

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