Alignment In Face Recognition Based On Deep Learning

For a long time,face recognition has been a research hotspot in the field of computer vision.The existing face recognition system has made a qualitative leap compared to a few years ago,and has been applied to various areas of life,such as security monitoring,access control face recognition,and mobile phone face unlocking.However,the existing face recognition system still has many shortcomings,for example,the face recognition performance of different domains is too different;in the scenes with occlusion,noise interference and too dark,the face recognition performance is significantly reduced;and cannot effectively deal with the situation that the detection performance is too low.In recent years,the popular reinforcement learning has become an emerging research field through the combination of deep learning models,and has shown extraordinary performance.From the perspective of reinforcement learning,this paper analyzes the reasons for the decline in face recognition model performance,which mainly comes from the lack of robustness of traditional face alignment methods.Therefore,this article is based on reinforcement learning to find a solution.The main work includes the following three points:1.In a distributed scenario,this paper designs and implements a new type of reinforcement learning platform,and elaborates on the composition and implementation of the main modules.At the same time,its operating performance is optimized,and the algorithm support part is modularized and implemented.2.The face alignment task is abstracted on the reinforcement learning application platform,and the searching of standard point and the searching of similar transformation matrix are realized.For the standard point search,this paper uses the approximate strategy optimization algorithm to search,and the performance is improved.For the similarity transformation matrix search,this paper completes the improvement of the similarity transformation matrix by indirectly searching the scale transformation coefficients.3.A progressive search method is proposed to improve the search performance of reinforcement learning.In addition,this paper implements search tasks under different reinforcement learning algorithms,and compares their effects on the final performance.The experimental results show that the standard point search and similarity matrix search strategies proposed in this paper improve the shortcomings of the existing alignment methods,and effectively improve the performance of the face recognition model on multiple test sets.