Robust Face Recognition Based On Pose And Forgery Detection

Face recognition is a hot spot and important landing scene in artificial intelligence and com-puter vision research.Although considerable progress has been made in recent years,its robustness still needs to be improved.Among the many factors that affect the robustness of face recognition,large-angle pose and forged face attacks are very common,which will directly lead to partial loss of face information and unreliable sources of face information,which is especially difficult to deal with.Although there are methods to control the quality of the face before recognition by detect-ing face attributes,thereby improving the robustness of face recognition,the disadvantage is that it does not simultaneously consider large-angle poses and fake face attacks to bring the robust-ness of face recognition.As a result,the accuracy of posture estimation and anti-counterfeiting algorithms is not good enough,and the new ways of forging faces are not defensive.This paper proposes a better head pose estimation and face anti-counterfeiting algorithm to implement robust face recognition technology based on pose and authenticity priors,and is dedicated to improving the robustness of face recognition.The main content and research results are as follows:1.This paper proposes an accurate and lightweight head pose estimation network that takes a single frame of RGB image as input and does not require face key points.A feature decou-pling module is proposed,in which the channel attention mechanism is used to adaptively adjust the response of different angle category feature maps at the channel level,so as to learn more distinguishing features for each angle.Then,the cross-category center loss is proposed to constrain the distribution of different angle categories in the latent variable sub-space,and a more compact and specific feature subspace can be obtained.By introducing inverse residual block and global average pooling,the amount of model parameters is greatly reduced.The accuracy of this algorithm is better than the latest methods on multiple public datasets,and the network structure is very lightweight.2.A more accurate and generalized face anti-counterfeiting algorithm is designed.The high-resolution backbone network is used to extract image features,and high-resolution and low-resolution depth features are merged in the classification head to achieve better capture of image details.It is proposed to reintegrate data labels into a more fine-grained hierarchical structure,and draw two parts of output from the main network,coarse and fine,and train the network in a supervised way from coarse to fine level,so that the network can learn more distinguishing features among real faces and various fake faces.A Two-stream framework is proposed,and the input sampler and quality invariant loss are designed to improve the algorithm's robustness to quality changes.The accuracy and generalization ability of this algorithm in multiple public data sets exceeds some methods in recent years,and ranks tenth in the FaceForensics Benchmark.3.A face recognition technology that is robust to pose changes and forged face attacks is re-alized.First,by improving the current advanced face detection and face recognition algo-rithms to weigh the network accuracy and the size of the model parameters,the 1:N face recognition function is completed.Secondly,two semantic perception modules are con-structed for posture detection and forgery detection to perceive the corresponding informa-tion of the input image during the face recognition process.Finally,a robust face recognition technology based on posture and authenticity priori is implemented,which is responded to the detected attribute priors,and robust face recognition can be performed in image and video scenes.