| Face detection and recognition has led to face payment,making it easier to track down criminals and find lost children and the elderly.Deep learning theory and algorithm realization make face recognition technology mature day by day.However,in real life scenes,especially in the context of this year’s epidemic,people often wear masks,glasses(sunglasses)and other parts of the face cover,resulting in feature loss and resulting in a sharp decline in the recognition accuracy of face recognition algorithms.How to eliminate the influence of occlusion area on face recognition,improve the accuracy of face recognition,enhance the robustness of the algorithm has become an urgent problem to be solved!This paper proposes and studies the improved face occlusion recognition algorithm from two aspects:first,based on the existing face features,the influence of occlusion area on face recognition can be eliminated by adjusting and improving the feature weight of unoccluded area and reducing the feature weight of occluded area accordingly;Second,the occluded area of occluded face image is repaired first,and then the repaired image is sent to face recognition network to realize reliable occluded face recognition.The main work revolves around the following two aspects.1.To solve the face recognition method based on feature extraction in the case of keep out area is larger the low accuracy problem in Inception improvement on the basis of the network structure of the space and channel characteristics put forward a kind of fusion of partial shade face recognition algorithm,the algorithm can image spatial characteristics and channel characteristic,as the key is extracted.The performance of the four related algorithms and the proposed algorithm is tested experimentally.In the case of no occlusion,the recognition accuracy of the proposed algorithm is only 0.11%lower than ArcFace.In the case of glasses occlusion,the recognition accuracy of the proposed algorithm is only 2.91%lower than MobileFaceNet.However,the algorithm model is only 3.5m,and the mask occlusion recognition accuracy is 7.99%higher than VGGFace,and the scarf occlusion accuracy is 10.42%higher than ArcFace.2.In terms of occluded face image repair,the performance of representative image repair algorithms GAN,DCGAN and WGAN is compared and analyzed.After comprehensive evaluation,WGAN was selected,global discriminator and local discriminator were added into the whole algorithm,symmetric loss,content loss and structure loss were introduced into the loss function,and u-NET model was used in the generator network structure,which ensured the integrity of the whole face image and semantic continuity.Therefore,this paper adopts Structual Similarity(SSIM)and Peak signal-to-noise Ratio(PSNR)to evaluate the image quality after restoration.Experimental results show that the proposed algorithm has the best recognition performance compared with other algorithms(VGG16,ArcFace,etc.)improved their recognition accuracy by-0.63%,1.85%,0.77%,1.82%and 3.19%,respectively.Therefore,it can be seen that the recognition accuracy of the proposed algorithm is slightly lower than ArcFace without occlusion,but with the increase of occlusion area,the recognition accuracy of the proposed algorithm keeps improving.The experimental results show that the algorithm optimization strategy proposed in this paper has certain advantages and good stability to solve the problem of face recognition accuracy reduction caused by the loss of face features caused by occlusion.It can further broaden the application scenarios of face recognition technology and has strong practical value. |
