Silent living body detection method

Abstract

The invention discloses a silent living body detection method. The silent living body detection method sequentially comprises the following steps: 1, preprocessing operation is performed on an input video to obtain a face picture with a fixed size; 2, the face image with the fixed size obtained in the step 1 is represented by RGB and YCrCb color spaces, and then the face image is input into a double-flow feature fusion network model to achieve feature extraction; and step 3, a non-living body category is subdivided into a printing attack category and a display attack category, category prediction is performed according to the image feature representation obtained in the step 2, and a multi-classification cross entropy loss supervision model is adopted for training, so that the classification error rate of the algorithm can be effectively reduced, and the generalization performance can be improved. According to the invention, single-frame picture input is adopted, additional auxiliary equipment is not needed, the algorithm implementation cost is low, additional man-machine interaction is not needed, and good user experience is achieved.

Description

technical field [0001] The invention relates to the field of human face living body detection, in particular to a silent living body detection method. Background technique [0002] It is urgent to introduce silent liveness detection into the system at a limited cost under the existing equipment conditions, so as to improve the security of the system. However, the existing algorithms generally regard live face detection as a binary classification task, ignoring the differences between different non-live attack images and the adverse effects of category imbalance on model learning. The trained models often have insufficient generalization performance and are difficult Applied to complex and changeable actual scenes. Contents of the invention [0003] In order to solve the above problems, the present invention discloses the purpose of the invention: the purpose of the present invention is to solve the shortcomings in the existing silent living body detection method based on ...

AI Technical Summary

Problems solved by technology

However, the existing algorithms generally regard live face detection as a binary classification task, ignoring the differences between different non-live attack images and the adverse effects of category imbalance on model learning. The trained models often have insufficient generalization performance and are difficult Applied to complex and changeable actual scenarios

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