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

technical field [0001] The invention belongs to the field of biological authentication anti-counterfeiting, and in particular relates to a face anti-counterfeiting detection method based on an attention model-based two-way convolutional neural network. Background technique [0002] With the development of technology, human beings use a variety of biometrics as important credentials for authentication systems, such as fingerprints, faces, voices, pupils, etc. The human face is one of the most influential biological characteristics, both economically and socially. In addition, due to the rapid development of face recognition and face detection, this technology has been applied in many occasions, ranging from access control systems in confidential places, to log-in systems for laptops, and even unlocking systems for mobile terminals, and other Compared with biometric features, face authentication has gradually become the most commonly used authentication method. [0003] Spoo...

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 amount of training data for model training, and the powerful learning ability also reduces the generalization of the model to a certain extent

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