Research On Face Recognition Algorithm Under Partial Occlusion

With the rapid development of artificial intelligence,identity authentication technology is an important research topic now.Among them,face recognition technology,as an emerging technology,has attracted much attention in many fields.With the development of face recognition technology,new algorithms are constantly flowing in,which makes the accuracy of face recognition under controlled conditions getting higher and higher.However,in real life,face recognition technology often faces some difficulties.For example,the effects of occlusion,lighting,facial expressions,and postures cause the effect of face recognition to be unsatisfactory.Partial occlusion will cause the accuracy of face recognition to drop sharply.Therefore,this article starts a discussion on partial occlusion face recognition.The proposed algorithm improves the accuracy of local occlusion face recognition:Firstly,the image enhancement technology and image denoising technology in face image preprocessing technology are introduced,and the theory of convolutional neural network is also introduced.An LLE-CNN face detection method for partially occluding faces is proposed.This method can detect Faces that are partially occluded and can be marked with occlusion.Secondly,based on the classification of occluded face recognition algorithms,the traditional subspace method is introduced.The sparse representation classification method and collaborative representation classification method in the traditional subspace method are two commonly used algorithms for local occlusion face recognition.Later,because the sparse representation classification method is not sufficient to describe coding errors in practice,a sparse representation classification is proposed The improved algorithm,robust sparse representation,compares the performance of the three algorithms in the AR face database.Finally,in order to improve the robustness of local occlusion face recognition.This paper proposes an algorithm based on deep feature dictionary representation classification.This algorithm uses a convolutional neural network as a featureextractor,then uses a dictionary to linearly encode the extracted deep features,and finally uses an improved algorithm for sparse representation coding.Robust sparse coding Residual classification.Experiments show that the algorithm can be robust to face recognition under both occluded and unoccluded conditions,is computationally effective,and robust to large continuous occlusions in face images.