Mask face living body detection method based on support vector machine

Abstract

The invention discloses a mask face living body detection method based on a support vector machine, and the method comprises the steps: carrying out the positioning of face key points through a face key point positioning algorithm based on a multi-stage cascade regression tree, and automatically generating mask face samples in batches according to the positioning points; constructing a face library without a mask and a face library with a mask; performing face recognition; extracting an LBP feature of the face image, and converting an LBP feature matrix into a histogram vector as a texture feature vector for living body detection; training and storing a support vector machine model based on texture features; carrying out living body detection; and outputting a verification result according to the face recognition result and the living body detection result. The face key point model is trained, key points of the nose bridge and the chin of the face can be positioned, and whether the face wears a mask or not is judged by detecting whether the key points are shielded or not; according to the method, the support vector machine is trained, a rapid in-vivo detection function is realized, the function is simultaneously suitable for an ordinary face and a face with a mask, the rapidity and accuracy of face recognition are not influenced, and the safety of the system is improved.

Description

technical field [0001] The invention relates to the technical fields of machine vision, image processing and pattern recognition, in particular to machine vision, image processing and pattern recognition. Background technique [0002] At present, the access control system based on face recognition has a wide range of market applications. Under the background of the global spread of the virus, wearing masks has become the new normal for people to travel. Therefore, the market demand for mask face recognition system is proposed. [0003] Mask face recognition has been a research hotspot in the past two years, and companies or scholars at home and abroad are doing research. For example, Py-ramidBox-Lite, launched by Baidu based on the deep learning tool-Flying Paddle, is a mobile terminal model that uses FaceBoxes as the Backbone network. In the actual application scenario, it is difficult to distinguish facial textures due to unclear facial features, low image resolution, an...

AI Technical Summary

Problems solved by technology

[0005] However, like traditional face recognition systems, mask face recognition systems also face the risk of being attacked by prosthetic faces

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