Face Recognition And Applied Research Based On Deep Learning

The development of deep learning promotes the development of face recognition technology applications,and different scenarios often have different problems.In the face recognition process of video surveillance,the low pixel resolution of the camera itself,or the low pixel resolution of the detected face due to the distance between the face and the camera,limits the recognition effect of face recognition.At the same time,the LFW as an example of the foreign face data set,there is a problem of recognition deviation when training the model,the recognition accuracy for European and American faces is high,and the accuracy when recognizing non-European and American faces will decrease.This article will take the face recognition of campus bayonet as an example to study and apply face recognition.To solve the problem of low-resolution face images,this paper uses super-resolution reconstruction algorithm to reconstruct low-resolution images into high-resolution images,and selects a reconstruction algorithm based on generated adversarial networks to improve the real effect of the reconstructed images.At the same time,for the problem that the reconstruction algorithm is easy to lose image information during the reconstruction process,the Laplacian pyramid is introduced into each layer of the neural network model to supplement the high-frequency information of the image from low resolution to high resolution,so as to obtain better Reconstruction effect.At the same time,in order to optimize the network structure,improve the excess loss function,and improve the model's robustness to outliers.Aiming at the problem of face recognition deviation in the models trained by foreign face data sets,this article will build a custom face data set,collect and process face images,reasonably adjust the proportion of different races in the data set,and improve the training model Recognition accuracy of local faces.Finally,a face recognition model based on improved reconstruction algorithm is implemented,and the influence of improved factors on the model is analyzed.Experiments show that the image reconstruction effect of the improved reconstruction algorithm is better than other reconstruction algorithms in subjective evaluation and objective evaluation.At the same time,the accuracy of model recognition based on the improved reconstruction algorithm is also improved by about 1%.And the accuracy rate of face recognition of the model trained with the customized face data set is about 0.9% higher than other foreign face data sets.