這一類方法會碰著的問題:However, since face images have similar structure, the image vectors are correlated, and any image in the sample space can be represented in a lower-dimensional subspace without losing a significant amount of information.

方式2的特點/長處:Thus, by applying this method, we find the projection directions that on one hand maximize the Euclidean distance between the face images of different classes and on the other minimize the distance between the face images of the same class. This ratio is maximized when the column vectors of the projection matrix W are the eigenvectors of ....



RECENTLY翻譯社 due to military, commercial, and law enforcement applications, there has been much interest in automatically recognizing faces in still and video images. This research spans several disciplines such as image processing, pattern recognition, computer vision, and neural networks. The data come from a wide variety of sources. One group of sources is the relatively controlled format images such as passports, credit cards, photo IDs, drivers’ licenses, and mug shots.


方法2的相關研究論文四篇:In the last decade numerous methods have been proposed to solve this problem翻譯社 Tian et al. [9] used the Pseudoinverse method by replacing ... with its pseudoinverse. The Perturbation method is used in [2] and [10]翻譯社 where a small perturbation matrix  is added to SW in order to make it nonsingular. Cheng et al. [11] proposed the Rank Decomposition method based on successive eigendecompositions of the total scatter matrix ST and the between-class scatter matrix SB.

方式2約略的描述:This method overcomes the limitations of the Eigenface method by applying the Fisher’s Linear Discriminant criterion. This criterion tries to maximize the ratio(數學式子,省略)where SB is the between-class scatter matrix翻譯社 and SW is the within-class scatter matrix.

方式弱點1.1:This method is an unsupervised technique since it does not consider the classes within the training set data. In choosing a criterion that maximizes the total scatter, this approach tends to model unwanted within-class variations such as those resulting from the differences in lighting, facial expression翻譯社 and other factors [6]翻譯社 [7].




方式4:Last, the Direct-LDA method is proposed in [12].

方式描寫:In this method翻譯社 at first翻譯社 PCA is applied to remove the null space of ST 翻譯社 which contains the intersection of the null spaces of SB and SW. Then翻譯社 the optimal projection vectors are found in the remaining lowerdimensional space by using the Null Space method. The difference between the Fisherface method and the PCA+Null Space method is that for the latter翻譯社 the within-class scatter matrix in the reduced space is typically singular. This occurs because all eigenvectors corresponding to the nonzero eigenvalues of ST are used for dimension reduction.

方法3.1和3.2的錯誤謬誤:Although翻譯社 the PCA+Null Space method and the variation proposed by Yang et al., use the original sample space, applying PCA and using all eigenvectors corresponding to the nonzero eigenvalues make these methods impractical for face recognition applications when the training set size is large. This is due to the fact that the computational expense of training becomes very large.

In this paper, a new method is proposed which addresses the limitations of other methods that use the null space of SW to find the optimal projection vectors.

這篇文章的導論一開始講利用,上面的論述比力一般化,後面的敘述將規模縮小:A more challenging class of application imagery includes real-time detection and recognition of faces in surveillance video images, which present additional constraints in terms of speed and processing requirements [1].

It is a difficult problem as there are numerous factors such as 3D pose, facial expression, hair style, make up, etc.翻譯社 which affect the appearance of an individual’s facial features. In addition to these varying factors, lighting, background, and scale changes make this task even more challenging. Additional problematic conditions include noise, occlusion, and many other possible factors.

其他改進2:Chen et al. also proved that by applying this method翻譯社 the modified Fisher’s Linear Discriminant criterion attains its maximum.

However翻譯社 they did not propose an efficient algorithm for applying this method in the original sample space. Instead, a pixel grouping method is applied to extract geometric features and reduce the dimension of the sample space. Then, they applied the Null Space method in this new reduced space.

方法描述:This method uses the simultaneous diagonalization method [8]. First翻譯社 the null space of SB is removed and翻譯社 then翻譯社 the projection vectors that minimize the within-class scatter in the transformed space are selected from the range space of SB.

方式3.1:Another novel method翻譯社 the PCA+Null Space method, was proposed by Huang et al. in [15] for dealing with the small sample size problem.


相幹方式出來了:Among these methods, appearance-based approaches operate directly on images or appearances of face objects, and process the images as two-dimensional holistic patterns. In these approaches, a two-dimensional image of size w by h pixels is represented by a vector in a wh-dimensional space. Therefore, each facial image corresponds to a point in this space. This space is called the sample space or the image space, and its dimension typically is very high [4]. 注重:對這些方法的描寫悉數用此刻式,這是因為這些方法現在進行也是如許(事實)。


The Linear Discriminant Analysis (LDA) method is proposed in [6] and [7]. (這裡用如今式有點不太准確,我會建議用曩昔式,因為這兩篇論文的提出已過去了。


臉部辨識為什麼很艱巨,它們遭到哪些因素的影響:This task is complex and can be decomposed into the smaller steps of detection of faces in a cluttered background, localization of these faces followed by extraction of features from the face regions翻譯社 and, finally, recognition and verification [2].

其他改良1:It has been shown that the original Fisher’s Linear Discriminant criterion can be replaced by the modified Fisher’s Linear Discriminant criterion in the course of solving the discriminant vectors of the optimal set in [18].



方式2的問題:In face recognition tasks, this method cannot be applied directly since the dimension of the sample space is typically larger than the number of samples in the training set. As a consequence翻譯社 SW is singular in this case. This problem is also known as the “small sample size problem” [8].

針對上面弱點的改進:However翻譯社 in order to make SW nonsingular翻譯社 some directions corresponding to the small eigenvalues of ST are thrown away in the PCA step. Thus翻譯社 applying PCA for dimensionality reduction has the potential to remove dimensions that contain discriminative information [12]翻譯社 [13], [14], [15], [16]. Chen et al. [17] proposed the Null Space method based on the modified Fisher’s Linear Discriminant criterion(數學式子,省略)This method has been proposed to be used when the dimension of the sample space is larger than the rank of the within-class scatter matrix SW.

下面我就用一篇2006年頒發在IEEE trans. on Pattern Analysis and Machine Intelligence上面的一篇文章Discriminative Common Vectors for Face Recognition來跟大師做申明,因為這篇講「臉部辨識」的文章是長文,導論的部分有點長:

方法3.2:Yang et al. applied a variation of this method in [16]. After dimension reduction, they split the new within-class scatter matrix, ... (where PPCA is the matrix whose columns are the orthonormal eigenvectors corresponding to the nonzero eigenvalues of ST ), into its null space .... Then翻譯社 all the projection vectors that maximize the betweenclass scatter in the null space are chosen. If翻譯社 according to some criterion翻譯社 more projection vectors are needed翻譯社 the remaining projection vectors are obtained from the range space.

Many methods have been proposed for face recognition within the last two decades [1], [3]. (提出什麼方式都用現在完成式來表達)

下面最先界說技術問題:Face recognition can be defined as the identification of individuals from images of their faces by using a stored database of faces labeled with people’s identities.


本研究的限制:Thus, the proposed method can be only used when the dimension of the sample space is larger than the rank of SW.

The remainder of the paper is organized as follows: (這一句話是科技陳腔濫調,照著寫就對了;也請注意標點符號翻譯

In Section 2, the Discriminative Common Vector approach is introduced. In Section 3, we describe the data sets and experimental results. Finally, we formulate our conclusions in Section 4.


一些視察:In our experiments, we observed that the performance of the Null Space method depends on the dimension of the null space of SW in the sense that larger dimension provides better performance. Thus, any kind of preprocessing that reduces the original sample space should be avoided.

解決問題的方法1:The Eigenface method has been proposed for finding such a lowerdimensional subspace [5]. The key idea behind the Eigenface method, which uses Principal Component Analysis (PCA), is to find the best set of projection directions in the sample space that will maximize the total scatter across all images such that (數學式子,省略)is maximized. Here翻譯社 ST is the total scatter matrix of the training set samples, and W is the matrix whose columns are the orthonormal projection vectors. The projection directions are also called the eigenfaces. Any face image in the sample space can be approximated by a linear combination of the significant eigenfaces. The sum of the eigenvalues that correspond to the eigenfaces not used in reconstruction gives the mean square error of reconstruction.

作法:In this method, all image samples are first projected onto the null space of SW, resulting in a new within-class scatter that is a zero matrix. Then, PCA is applied to the projected samples to obtain the optimal projection vectors.

方式缺點1.2:Additionally, since the criterion does not attempt to minimize the withinclass variation, the resulting classes may tend to have more overlap than other approaches. Thus, the projection vectors chosen for optimal reconstruction may obscure the existence of the separate classes.

相幹研究的缺點:However, the above methods are typically computationally expensive since the scatter matrices are very large (e.g., images of size 256 by 256 yield scatter matrices of size 65,536 by 65,536). Swets and Weng [7] proposed a two stage PCA+LDA method, also known as the Fisherface method, in which PCA is first used for dimension reduction so as to make SW nonsingular before the application of LDA.

方式的缺點:However, removing the null space of SB by dimensionality reduction will also remove part of the null space of SW and may result in the loss of important discriminative information [13], [15], [16]. Furthermore翻譯社 SB is whitened as a part of this method. This whitening process can be shown to be redundant and, therefore, should be skipped.(講方法的缺點就是為了「轉」到自己的研究。


文章來自: http://hjlee0301.pixnet.net/blog/post/18977428-%E8%AB%96%E6%96%87%E5%B0%8E%E8%AB%96%E7%9A%84%E5%AF%A有關各國語文翻譯公證的問題歡迎諮詢萬國翻譯公司02-23690931

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