Question

中文翻译成英语，内容如下，别用在线翻译糊弄我，谢谢。急！

基于NMF的人脸识别算法研究

摘要：
人脸识别是一项极具挑战性的课题，它汇聚了多个学科的知识与技术，如信号处理、智能控制、模式识别、机器视觉等。
非负矩阵分解(Non-negative Matrix Lee Factorization,NMF)算法是通过对矩阵引入非负性约束，使得重建图像是由基图像非减的叠加组合而成，更符合人类思维中“局部构成整体”的概念。NMF方法更好的实现了人脸库的局部分量或部件的提取。但是和PCA，ICA方法一样，NMF方法无法消除光照，姿态等因素对识别的影响。本文中将小波分解和NMF方法相结合，来最大程度的减少光照、姿态等因素对识别的影响。同时，将NMF方法的识别结果与传统的PCA方法识别结果进行了比较，分析了两种方法的特点。指出NMF方法在局部特征提取和识别方面具有较好的效果。

关键词： 子空间 人脸识别 非负矩阵分解

Answer 1

这段摘要难度相当高，下面的翻译出于人工，请楼主费心审阅，可以放心采用

基于NMF的人脸识别算法研究
Research on the Face Recognition Algorithm Based on NMF
摘要：
人脸识别是一项极具挑战性的课题，它汇聚了多个学科的知识与技术，如信号处理、智能控制、模式识别、机器视觉等。
非负矩阵分解(Non-negative Matrix Lee Factorization,NMF)算法是通过对矩阵引入非负性约束，使得重建图像是由基图像非减的叠加组合而成，更符合人类思维中“局部构成整体”的概念。NMF方法更好的实现了人脸库的局部分量或部件的提取。但是和PCA，ICA方法一样，NMF方法无法消除光照，姿态等因素对识别的影响。本文中将小波分解和NMF方法相结合，来最大程度的减少光照、姿态等因素对识别的影响。同时，将NMF方法的识别结果与传统的PCA方法识别结果进行了比较，分析了两种方法的特点。指出NMF方法在局部特征提取和识别方面具有较好的效果。
Abstract: Face recognition is a challenging subject, which brings together multi-disciplinary knowledge and techniques, such as signal processing, intelligent control, pattern recognition, machine vision.
NMF (Non-negative Matrix Lee Factorization, NMF) algorithm makes the reconstructed image is composed of the non-reduced superposition of the base images by introducing non-negative constraint on the matrix, thereby more in line with the concept "Parts form an integral part" existed in human thinking. NMF method better realizes the extraction of local components or parts of the face base. However, like PCA, ICA methods, NMF method can not eliminate the influence of the factors, such as illumination, pose, on recognition. This paper combines wavelet decomposition with NMF method to reduce the influence of illumination, pose, etc. on recognition in the greatest degree. Meanwhile, the recognition results with NMF method are compared with those using traditional PCA method to analyze the characteristics of the two methods. It is pointed out that NMF method offers better effects in the extraction and recognition of local features.

关键词： 子空间 人脸识别 非负矩阵分解
Key words: subspace; face recognition; non-negative matrix Lee factorization

Answer 2

Abstract:

Face recognition is a challenging task, it gathers the multiple disciplines knowledge and technologies, such as signal processing and intelligent control, pattern recognition and machine visual, etc.

Nonnegative matrices decomposition (Non - negative Matrix Factorization Lee him self, the NMF is passed on the Matrix) algorithm is introduced, makes a nonnegative property constraints rebuilding image is not decreased by base images into superposition combination, more accord with human thought "local integral" concept. Better realize the NMF method of face library of local component or components extraction. But and PCA, ICA method is same, NMF methods cannot eliminate illumination, attitude to the influence of factors such as identification. This paper will wavelet decomposition and NMF method unifies, to minimising illumination, attitude to the influence of factors such as identification. Meanwhile, the recognition results and the NMF methods of traditional PCA method recognition results were compared and analyzed the characteristics of the two methods. Points out the NMF method in local feature extraction and recognition has good effect.

Answer 3

Research of Algorithm for Face Recognition Based on NMF
Abstract: Face recognition is a challenging study which needs knowledge and technology in many subjects, such as signal processing, intelligent controlling, pattern recognition, machine vision and so on.
With introducing non-negative constraints to matrix, NMF(Non-negative Matrix Lee Factorization) makes the reconstructed image constituted by superposition of the basic images, which matchs the concept of parts constitute overall better. NMF method extracted partial face database component or components better. However, the same with PCA and ICA, NMF method can not eliminate the effects of light, gesture and other factors. In this paper, the methods combination of wavelet decomposition and NMF Minimize the impact of light, gesture and other factors to the recognition. At the same time, compared the recognition results of NMF method and traditional PCA method, the paper analyzed the features of these two methods and pointed out that NMF method is better in local feature extraction and recognition.

Key words:Subspace, Face Recognition, NMF

Answer 4

A Research on NMF_based Algorithm for Human-face Recognition

Abstract,
Human-face recognition is a quite challenging subject, which demands multidisciplinary knowledge and technique such as those of signal processing, of intelligent controlling, of pattern recognition and of robot vision etc..
The Non-negative Matrix Lee Factorization (NMF) algorithm, through imposing non-negative constraints on Matrix(es), re-constructs a human-face image by a series of non-subtractive overlappings of Base Images, thus matching the natural logic in human reasoning--"parts comprise a whole". Comparing with the PCA or ICA method, NMF better extracts parts or components from human-face library, but it cannot eliminate the disturbances from lighting or posture etc. either.
This paper combines wavelet resolution and NMF to reduce to the maximum extent the influences from lighting or posture etc.. It also compares the recognition results of NMF with those of PCA, analyzes the traits of these two methods, and points out that NMF method can better extract and recognize partial or componental features of human face.

Keywords, Subspace, Human-face Recognition, Non-negative Matrix Lee Factorization