Research On Improvement Algorithm Of Face Recognition Based On Manifold Learning

Comparing with other biometric identification technologies,face recognition technology is one of the most potential identification technologies because its advantages of direct and safe.It is a key step to identify and requires the reduction of the dimension of the data in the internal structure of face image data retention characteristics of the same.At present,the manifold learning algorithm is a hot research topic in the field of pattern recognition,the main idea of which is to find a low dimensional manifold in the high-dimensional data space,and get the corresponding embedding in order to achieve the purpose of dimension reduction.In this paper,we focused on study of the manifold learning algorithm and face recognition.Our main works follow as:1.Due to the manifold learning algorithm exists the question of noise sensitivity,an improved threshold function noise reduction algorithm is improved based on the traditional hard and soft threshold algorithm.Compared with the traditional threshold noise reduction algorithm,this algorithm overcomes the discontinuity of traditional algorithm in the process of image de-noising,and the questions of constant deviation.In the simulation experiment of noise reduction,compared with the traditional noise reduction algorithm,the improved algorithm gets more quality of image.2.Combining the Cam weight distance with the local preserving projection algorithm and the orthogonal preserving projection algorithm,this paper improved Cam weight local preserving projection method and an Cam weight orthogonal preserving method.The Euclidean distance used to these improved algorithms is instead of the Cam weight distance,which improve the performance ofthe algorithms.These improved algorithms are used to simulate the face image.Compared with the original two manifold learning algorithms,the performance of these improved algorithms is better.