Human face anti-counterfeiting detection method of double-channel convolutional neural network based on attention model

Abstract

The invention discloses a face anti-counterfeiting detection method of a double-channel convolutional neural network based on an attention model, and the method comprises the steps: constructing a training set which comprises a training sample composed of an RGB face image and an MSR face image; constructing a face anti-counterfeiting detection network which comprises a feature extraction unit used for extracting RGB feature vectors and MSR feature vectors, a feature fusion unit used for fusing the RGB feature vectors and the MSR feature vectors, and a feature classification unit used for classifying the fused feature vectors; training the face anti-counterfeiting detection network by using the training set to obtain a face anti-counterfeiting detection model; during application, preprocessing a to-be-detected RGB face picture and then inputting the preprocessed face image into the face anti-counterfeiting detection model, and outputting a detection result, namely a true face or a false face, through calculation. According to the method, the influence of illumination on detection and rich texture information of the RGB picture can be reduced by using the MSR picture at the same time, so that the detection result is accurate and has certain generalization.

Description

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AI Technical Summary

Problems solved by technology

The neural network can learn more distinguishing features to judge face spoofing attacks, but the deep learning method requires a large amo

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