Fusion of appearance-based face recognition algorithms

Although many algorithms have been proposed, face recognition and verification systems can guarantee a good level of performances only for controlled environments. In order to improve the performance and robustness of face recognition and verification systems, multi-modal and mono-modal systems based on the fusion of multiple recognisers using different or similar biometrics have been proposed, especially for verification purposes. In this paper, a recognition and verification system based on the combination of two well-known appearance-based representations of the face, namely, principal component analysis (PCA) and linear discriminant analysis (LDA), is proposed. Both PCA and LDA are used as feature extractors from frontal view images. The benefits of such a fusion are shown for different environmental conditions, namely, ideal conditions, characterised by a very limited variability of environmental parameters, and real conditions with a large variability of lighting, scale and facial expression.     

A two-dimensional Neighborhood Preserving Projection for appearance-based face recognition

This paper presents a two-dimensional Neighborhood Preserving Projection (2DNPP) for appearance-based face representation and recognition. 2DNPP enables us to directly use a feature input of 2D image matrices rather than 1D vectors. We use the same neighborhood weighting procedure that is involved in NPP to form the nearest neighbor affinity graph. Theoretical analysis of the connection between 2DNPP and other 2D methods is presented as well. We conduct extensive experimental verifications to evaluate the performance of 2DNPP on three face image datasets, i.e. ORL, UMIST, and AR face datasets. The results corroborate that 2DNPP outperforms the standard NPP approach across all experiments with respect to recognition rate and training time. 2DNPP delivers consistently promising results compared with other competing methods such as 2DLPP, 2DLDA, 2DPCA, ONPP, OLPP, LPP, LDA, and PCA.     

Two-dimensional FLD for face recognition

This paper presents a new scheme of face image feature extraction, namely, the two-dimensional Fisher linear discriminant. Experiments on the ORL and the UMIST face databases show that the new scheme outperforms the PCA and the conventional PCA+FLD schemes, not only in its computational efficiency, but also in its performance for the task of face recognition.     

Bimode model for face recognition and face representation

Tensorface based approaches decompose an image into its constituent factors (i.e., person, lighting, viewpoint, etc.), and then utilize these factor spaces for recognition. However, tensorface is not a preferable choice, because of the complexity of its multimode. In addition, a single mode space, except the person-space, could not be used for recognition directly. From the viewpoint of practical application, we propose a bimode model for face recognition and face representation. This new model can be treated as a simplified model representation of tensorface. However, their respective algorithms for training are completely different, due to their different definitions of subspaces. Thanks to its simpler model form, the proposed model requires less iteration times in the process of training and testing. Moreover bimode model can be further applied to an image reconstruction and image synthesis via an example image. Comprehensive experiments on three face image databases (PEAL, YaleB frontal and Weizmann) validate the effectiveness of the proposed new model.     

改进的模块PCA人脸识别新算法

由于传统的PCA要求训练样本符合高斯分布,而现实中得到的图片往往由于光照、表情、姿态的不同,不符合高斯分布。为了使PCA不再局限于高斯分布,并且不影响其识别率,提出一种改进的模块PCA人脸识别新算法。一方面,新算法采取了分块方式,将具有同一姿态的图片划分进同一矩阵,以使训练样本更接近于高斯分布。另一方面,新算法对传统PCA算法中前三个主分量加小于1的权重系数,可以减少光照变化对识别率的影响。利用分块和权重系数的共同作用使得PCA不再局限于高斯分布,同时提高识别率。最后在ORL人脸库上进行实验,结果表明新算法优于传统的PCA算法。     

A singular value decomposition representation based approach for robust face recognition

In the field of face recognition, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been widely used. Although both SRC and CRC have shown good classification results, it is still controversial whether it is sparse representation or collaborative representation that helps face recognition. In this paper, a new singular value decomposition based classification (SVDC) is proposed for face recognition. The proposed approach performs SVD on the training data of each class, and then determines the class of a test sample by comparing in which class of singular vectors it can be better represented. Experimental results on Yale B, PIE and UMIST datasets show that the proposed method achieves better recognition performance compared with several existing representation based classification algorithms. In addition, by adding Gaussian noise and Salt pepper noise to these datasets, it is proved that SVDC has better robustness. At the same time, the experimental results show that the recognition accuracy of the method acting on the training samples constructed by each class is higher than that of the method acting on the training sets constructed by all classes.

     

MLP-based face recognition with Gabor filters and PCA

In this paper, we present a multiple scale neural architecture for face recognition. The architecture is composed of several stages: face detection, Difference of Gaussians, Gabor filter bank, Principal Component Analysis, and two-stage MLPs. The architecture was evaluated using two well-known face databases. A detailed study of all the parameters that influence the architecture performance was carried out. The architecture achieved a correct detection rate of 84% with face images changing in pose and gesture.     

Two-dimensional discriminant transform for face recognition

This paper develops a new image feature extraction and recognition method coined two-dimensional linear discriminant analysis (2DLDA). 2DLDA provides a sequentially optimal image compression mechanism, making the discriminant information compact into the up-left corner of the image. Also, 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner. 2DLDA is tested and evaluated using the AT&T face database. The experimental results show 2DLDA is more effective and computationally more efficient than the current LDA algorithms for face feature extraction and recognition.     

Two-dimensional Laplacianfaces method for face recognition

In this paper we propose a two-dimensional (2D) Laplacianfaces method for face recognition. The new algorithm is developed based on two techniques, i.e., locality preserved embedding and image based projection. The 2D Laplacianfaces method is not only computationally more efficient but also more accurate than the one-dimensional (1D) Laplacianfaces method in extracting the facial features for human face authentication. Extensive experiments are performed to test and evaluate the new algorithm using the FERET and the AR face databases. The experimental results indicate that the 2D Laplacianfaces method significantly outperforms the existing 2D Eigenfaces, the 2D Fisherfaces and the 1D Laplacianfaces methods under various experimental conditions.     

一种融合PCA 和KFDA 的人脸识别方法

提出一种融合PCA和KFDA的人脸识别方法，即在进行非线性映射之前，首先利用经典的主分量分析(C—PCA)进行降维，然后执行KFDA．为进一步降低整个算法的计算时问，又提出一种I—PCA KFDA方法，它直接基于图像矩阵的主分量分析(I—PCA)．ORL标准人脸库的试验结果表明，与现有的核Fisher鉴别分析方法相比，两种方法可将特征抽取的速度分别提高3倍和7倍，其识别精度没有丝毫的降低．     

A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition

Recently, in a task of face recognition, some researchers presented that independent component analysis (ICA) Architecture I involves a vertically centered principal component analysis (PCA) process (PCA I) and ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). They also concluded that the performance of ICA strongly depends on its involved PCA process. This means that the computationally expensive ICA projection is unnecessary for further process and involved PCA process of ICA, whether PCA I or II, can be used directly for face recognition. But these approaches only consider the global information of face images. Some local information may be ignored. Therefore, in this paper, the sub-pattern technique was combined with PCA I and PCA II, respectively, for face recognition. In other words, two new different sub-pattern based whitened PCA approaches (which are called Sp-PCA I and Sp-PCA II, respectively) were performed and compared with PCA I, PCA II, PCA, and sub-pattern based PCA (SpPCA). Then, we find that sub-pattern technique is useful to PCA I but not to PCA II and PCA. Simultaneously, we also discussed what causes this result in this paper. At last, by simultaneously considering global and local information of face images, we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition. The experimental results reveal that the proposed novel hybrid approach has better recognition performance than that obtained using other traditional methods.     

二维典型相关分析及其在人脸识别中的应用

通过对传统的基于向量的典型相关分析(CCA)方法进行改进,提出了一种新的直接基于特征矩阵的二维典型相关分析方法(2DCCA),并将其应用于人脸识别的特征融合过程中。较基于向量的典型相关分析,该方法的优点主要有两点：第一,该方法计算过程中构造的协方差矩阵维数大幅度减小,这在一定程度上避免了人脸识别中存在的“高维小样本问题”;第二,由于协方差矩阵维数的缩减,使特征抽取的速度明显提高。最后在ORL标准人脸库和AR大型人脸数据库上的实验结果有效地验证了这两点。     

一种基于人脸分割的PCA和SVM人脸识别方法

基于PCA和SVM提出了一种新的人脸分割法,将双眼、额头、鼻子、嘴等可以明显表征人脸的六类特征提取出来,舍弃双颊以及耳朵等特征量少的部位。融合上述人脸的特征识别结果,运用支持向量机的方法进行分类识别,实验结果表明,文章所提出的PCA与SVM融合的新的人脸分割方法能有效地对人脸进行分类,极大地提升识别率。     

Discriminative sparse representation for face recognition

Recently Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition. In SRC, the testing image is expected to be best represented as a sparse linear combination of training images from the same class, and the representation fidelity is measured by the ?₂-norm or ?₁-norm of the coding residual. However, SRC emphasizes the sparsity too much and overlooks the spatial information during local feature encoding process which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the spatial information but overlooks the different discriminative ability in different face regions. In this paper, we propose to weight spatial locations based on their discriminative abilities in sparse coding for robust face recognition. Specifically, we learn the weights at face locations according to the information entropy in each face region, so as to highlight locations in face images that are important for classification. Furthermore, in order to construct a robust weights to fully exploit structure information of each face region, we employed external data to learn the weights, which can cover all possible face image variants of different persons, so the robustness of obtained weights can be guaranteed. Finally, we consider the group structure of training images (i.e. those from the same subject) and added an ?_2,1-norm (group Lasso) constraint upon the formulation, which enforcing the sparsity at the group level. Extensive experiments on three benchmark face datasets demonstrate that our proposed method is much more robust and effective than baseline methods in dealing with face occlusion, corruption, lighting and expression changes, etc.     

推广的PCA及其在人脸识别中的应用

基于传统的PCA方法,提出了推广的PCA人脸识别方法.推广的PCA方法先对训练图像矩阵集进行分块,再利用传统PCA对分块得到的子训练矩阵集进行分析,得到多个变换矩阵,通过这些变换矩阵将训练图片和测试图片投影到特征空间进行鉴别.与传统PCA方法相比,提高了主元的维数,有效地增加了识别的精度.在FERET人脸库上的试验结果表明,所提出的方法在识别性能上明显优于传统的PCA方法,识别率得到了提高.     

分块PCA及其在人脸识别中的应用

主成分分析(principal component analysis,PCA)是公认的特征抽取的最为重要的工具之一,目前仍然被广泛地应用在人脸等图像识别领域.基于PCA,提出了分块PCA的人脸识别方法.分块PCA方法先对图像进行分块,对分块得到的子图像利用PCA进行鉴别分析.其特点是能有效地抽取图像的局部特征,对人脸表情和光照条件变化较大的图像表现尤为突出.与PCA方法相比,由于使用子图像矩阵,分块PCA可以避免使用奇异值分解理论,过程简便.此外,PCA是分块PCA的特例.在Yale和NUST603人脸库上的试验结果表明,所提出的方法在识别性能上明显优于经典的PCA方法,识别率可以分别提高6.7和4.4个百分点.     

一种局部加权的二维主成分分析算法及其在人脸识别中的应用

提出了一种将局部特征加权与二维主成分分析相结合的局部加权的二维主成分分析方法．引入了二维局部加权特征子空间的概念，将各类样本映射到这个局部加权特征子空间，再通过计算测试样本到加权子空间的距离进行样本的分类．使用这种方法在ORL人脸库上进行测试，结果表明，经过局部特征加权的二维主成分分析方法比普通的二维主成分分析方法具有更优的性能，并且在提高识别率的同时算法的复杂程度并没有明显增加．     

Subspace evolution analysis for face representation and recognition

This paper develops a novel framework that is capable of dealing with small sample size problem posed to subspace analysis methods for face representation and recognition. In the proposed framework, three aspects are presented. The first is the proposal of an iterative sampling technique. The second is adopting divide-conquer-merge strategy to incorporate the iterative sampling technique and subspace analysis method. The third is that the essence of 2D PCA is further explored. Experiments show that the proposed algorithm outperforms the traditional algorithms.     

Multi-resolution dictionary collaborative representation for face recognition

Pattern Analysis and Applications - In this paper, a multi-resolution dictionary collaborative representation(MRDCR) method for face recognition is proposed. Unlike most of the traditional sparse...