Living person detection method applied to face recognition

Abstract

This invention discloses a living person detection method applied to face recognition. The method includes the following steps that: modeling is performed on the background of a current recognition environment; face detection is performed: the locations and sizes of faces are obtained and are adopted as the initial position of next face detection; the similarity between the background of inputted face images and a modeling background image is calculated through adopting an SSIM method; when the faces are detected, ultrasonic waves are utilized to measure the distance between a camera and an obstacle, and a classification probability value f is obtained through utilizing a logistic regression model; and an SSIM value and the value f are combined linearly, so that a final result R can be obtained, and when R is smaller than a certain threshold value, a spoofing attack is determined. The living person detection method applied to face recognition of the invention has the advantages of high success rate and excellent stability.

Description

technical field [0001] The invention belongs to the field of biological feature recognition security, in particular to a living body detection method applied to face recognition. Background technique [0002] With the rapid development and wide application of face recognition technology, its security has also been severely challenged. Spoofing attacks are still a fatal threat to face recognition systems. Generally speaking, in the field of face recognition, there are three methods of fraud: [0003] a. Use photos of legal users; [0004] b. Use videos of legitimate users; [0005] c. Use the 3D model of the legitimate user. [0006] Photo attack is the cheapest and most convenient method of deception, because face images are very easy to obtain, such as downloading from the Internet, secretly taking pictures with cameras, etc. Intruders can rotate, move and warp the photo to create a real-life look like a legitimate user to trick the facial recognition system. At prese...

AI Technical Summary

Problems solved by technology

In practical applications, the last category is the best, but it is not friendly and requires user participation

The second type of method needs to increase equipment and increase the cost of spending

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