Real-time Transmission Of Noisy Face Recognition

With the rapid development of artificial intelligence and big data technologies in recent years,face recognition technology has also become a hot topic.In face recognition technology,the most challenging issue is real-time transmission of face recognition.Because real-time transmission face recognition has almost strict real-time requirements on the algorithm requirements,because generally the field of application of this technology is the need to quickly identify the target face,identity classification,and friendly human-computer interaction.If the real-time performance is poor,people need to wait for the algorithm to run without a good customer experience.Therefore,before the image is identified,the image is first de-backgrounded.The background-going algorithm uses the single Gaussian function to establish the background model.Because the continuous images captured by nature generally obey the normal distribution,there is a basis for using a single Gaussian function.And because the single Gaussian function only needs to update two parameters when updating the background model,and the parameters are relatively simple,real-time performance is better than other algorithms.However,the background model established by the single Gaussian function does not have a good recognition of the background update of the mutation environment.Therefore,here we have added a clustering algorithm,the mean shift algorithm.Because the mean-shift clustering algorithm can perform background clustering on a continuous image,it can ensure the background model's self-renewal in the case of environmental mutation,and solve the shortcomings of the background model established by the single Gaussian function.This makes the overall algorithm have good real-time and accuracy in the recognition of foreground image.The next step is to identify and distinguish face images.When selecting algorithms,we must first consider real-time requirements.In this paper,sparse representation classification(SRC)is used to identify human faces.Because of the advantages of the SRC algorithm,it only needs to operate on the sparse matrix,which greatly simplifies the calculation of the algorithm.Because of the characteristics of the storage method of the sparse matrix,the memory requirements of the algorithm are relatively low,so the SRC algorithm also has a Good real-time performance.However,this algorithm does nothave a good solution to the problem of finding the global optimal solution for non-convex functions.It has certain limitations for the recognition of some single-sample,non-aligned faces.Therefore,this paper designs a series of optimization algorithms,named Sludge algorithm.The algorithm has a good solution to the problem of solving global optimal solutions of non-convex functions,allowing the lighting dictionary to learn autonomously,making the algorithm have a good recognition of the single sample face.The SRC algorithm has a very strong role in strengthening the weaknesses.In general,the entire system has good real-time performance when it recognizes people's faces.Compared with other algorithms,it also has a high recognition rate.