As a key technique in hyperspectral image pre-processing,dimensionality reduction has received a lot of attention.However,most of the graph-based dimensionality reduction methods only consider a single structure in th...As a key technique in hyperspectral image pre-processing,dimensionality reduction has received a lot of attention.However,most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures.In this paper,we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information.These two methods explore local information into the collaborative graph through competing constraints,thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm.By classifying four benchmark hyperspectral data,the proposed methods are proved to be superior to several advanced algorithms,even under small-sample-size conditions.展开更多
Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. ...Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.展开更多
The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SI...The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.展开更多
As a new dimension reduction method, the two-dimensional principal component (2DPCA) can be well applied in face recognition, but it is susceptible to outliers. Therefore, this paper proposes a new 2DPCA algorithm bas...As a new dimension reduction method, the two-dimensional principal component (2DPCA) can be well applied in face recognition, but it is susceptible to outliers. Therefore, this paper proposes a new 2DPCA algorithm based on angel-2DPCA. To reduce the reconstruction error and maximize the variance simultaneously, we choose F norm as the measure and propose the Fp-2DPCA algorithm. Considering that the image has two dimensions, we offer the Fp-2DPCA algorithm based on bilateral. Experiments show that, compared with other algorithms, the Fp-2DPCA algorithm has a better dimensionality reduction effect and better robustness to outliers.展开更多
Graph-based dimensionality reduction (DR) methods have been applied successfully in many practical problems, such as face recognition, where graphs play a crucial role in modeling the data distribution or structure....Graph-based dimensionality reduction (DR) methods have been applied successfully in many practical problems, such as face recognition, where graphs play a crucial role in modeling the data distribution or structure. However, the ideal graph is, in practice, difficult to discover. Usually, one needs to construct graph empirically according to various motivations, priors, or assumptions; this is inde- pendent of the subsequent DR mapping calculation. Different from the previous works, in this paper, we attempt to learn a graph closely linked with the DR process, and propose an al- gorithm called dimensionality reduction with adaptive graph (DRAG), whose idea is to, during seeking projection matrix, simultaneously learn a graph in the neighborhood of a pre- specified one. Moreover, the pre-specified graph is treated as a noisy observation of the ideal one, and the square Frobenius divergence is used to measure their difference in the objective function. As a result, we achieve an elegant graph update for- mula which naturally fuses the original and transformed data information. In particular, the optimal graph is shown to be a weighted sum of the pre-defined graph in the original space and a new graph depending on transformed space. Empirical results on several face datasets demonstrate the effectiveness of the proposed algorithm.展开更多
Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classifi...Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features' contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis (ICA) features. These local features are acquired using locally lateral subspace (LLS) strategy. Then, through linear discriminant analysis (LDA) we investigate the intraclass and interclass representation of each local ICA feature and express each feature's contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding (LLE) along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE, FERET and CK% is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects.展开更多
Marginal Fisher analysis (MFA) is a repre- sentative margin-based learning algorithm for face recognition. A major problem in MFA is how to select appropriate parameters, k1 and k2, to construct the respective intri...Marginal Fisher analysis (MFA) is a repre- sentative margin-based learning algorithm for face recognition. A major problem in MFA is how to select appropriate parameters, k1 and k2, to construct the respective intrinsic and penalty graphs. In this paper, we propose a novel method called nearest-neighbor (NN) classifier motivated marginal discriminant projections (NN-MDP). Motivated by the NN classifier, NN-MDP seeks a few projection vectors to prevent data samples from being wrongly categorized. Like MFA, NN-MDP can characterize the compactness and separability of samples simultaneously. Moreover, in contrast to MFA, NN-MDP can actively construct the intrinsic graph and penalty graph without unknown parameters. Experimental results on the 0RL, Yale, and FERET face databases show that NN-MDP not only avoids the intractability, and high expense of neighborhood parameter selection, but is also more applicable to face recognition with NN classifier than other methods.展开更多
An algorithm for face description and recognition based on multi-resolution with multi-scale local binary pattern (multi-LBP) features is proposed. The facial image pyramid is constructed and each facial image is di...An algorithm for face description and recognition based on multi-resolution with multi-scale local binary pattern (multi-LBP) features is proposed. The facial image pyramid is constructed and each facial image is divided into various regions from which partial and holistic local binary patter (LBP) histograms are extracted. All LBP features of each image are concatenated to a single LBP eigenvector with different resolutions. The dimensionaUty of LBP features is then reduced by a local margin alignment (LMA) algorithm based on manifold, which can preserve the between-class variance. Support vector machine (SVM) is applied to classify facial images. Extensive experiments on ORL and CMU face databases clearly show the superiority of the proposed scheme over some existed algorithms, especially on the robustness of the method against different facial expressions and postures of the subjects.展开更多
Isometric projection (IsoProjection) is a linear dimensionality reduction method, which explicitly takes into account the manifold structure embedded in the data. However, IsoProjection is non-orthogonal, which make...Isometric projection (IsoProjection) is a linear dimensionality reduction method, which explicitly takes into account the manifold structure embedded in the data. However, IsoProjection is non-orthogonal, which makes it extremely sensitive to the dimensions of reduced space and difficult to estimate the intrinsic dimensionality. The non-ortbogonality also distorts the metric structure embedded in the data. This paper proposes a new method called orthogonal isometric projection (O-IsoProjection), which shares the same linear character as IsoProjection and overcomes the metric distortion problem of IsoProjection. Similar to IsoProjection, O-lsoProjection firstly constructs an adjacency graph which can reflect the manifold structure embedded in the data and the class relationship between the sample points of face space, and then obtains the projections by preserving such a graph structure. Different from IsoProjection, O-IsoProjection requires the basis vectors to be orthogonal, and the orthogonal basis vectors can be calculated by iterative way. Experimental results on ORL and Yale databases show that O-lsoProjection has better recognition rate for face recognition than Eigenface, Fisherface and IsoProjection.展开更多
子空间特征提取是人脸识别中的关键技术之一,结合局部Fisher判别分析技术和张量子空间分析技术的优点,本文提出了一种新的张量局部Fisher判别分析(Tensor local Fisher discriminant analysis,TLFDA)子空间降维技术.首先,通过对局部Fis...子空间特征提取是人脸识别中的关键技术之一,结合局部Fisher判别分析技术和张量子空间分析技术的优点,本文提出了一种新的张量局部Fisher判别分析(Tensor local Fisher discriminant analysis,TLFDA)子空间降维技术.首先,通过对局部Fisher判别技术进行分析,调整了其类间散度目标泛函,使算法的识别性能更高且时间复杂度更低;其次,引入张量型降维技术对输入数据进行双边投影变换而非单边投影,获得了更高的数据压缩率;最后,采用迭代更新的方法计算最优的变换矩阵.通过ORL和PIE两个人脸库验证了所提算法的有效性.展开更多
基金supported by the National Natural Science Foundation of China(No.41601344)the Fundamental Research Funds for the Central Universities(Nos.300102320107 and 201924)+2 种基金the National Key Research and Development Project(No.2020YFC1512000)in part by the General Projects of Key R&D Programs in Shaanxi Province(No.2020GY-060)Xi’an Science&Technology Project(Nos.2020KJRC0126 and 202018)。
文摘As a key technique in hyperspectral image pre-processing,dimensionality reduction has received a lot of attention.However,most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures.In this paper,we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information.These two methods explore local information into the collaborative graph through competing constraints,thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm.By classifying four benchmark hyperspectral data,the proposed methods are proved to be superior to several advanced algorithms,even under small-sample-size conditions.
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.
基金Supported by the National Natural Science Foundation of China (No.61571222)the Natural Science Research Program of Higher Education Jiangsu Province (No.19KJD520005)+1 种基金Qing Lan Project of Jiangsu Province (Su Teacher’s Letter 2021 No.11)Jiangsu Graduate Scientific Research Innovation Program (No.KYCX21_1944)。
文摘The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.
文摘As a new dimension reduction method, the two-dimensional principal component (2DPCA) can be well applied in face recognition, but it is susceptible to outliers. Therefore, this paper proposes a new 2DPCA algorithm based on angel-2DPCA. To reduce the reconstruction error and maximize the variance simultaneously, we choose F norm as the measure and propose the Fp-2DPCA algorithm. Considering that the image has two dimensions, we offer the Fp-2DPCA algorithm based on bilateral. Experiments show that, compared with other algorithms, the Fp-2DPCA algorithm has a better dimensionality reduction effect and better robustness to outliers.
文摘Graph-based dimensionality reduction (DR) methods have been applied successfully in many practical problems, such as face recognition, where graphs play a crucial role in modeling the data distribution or structure. However, the ideal graph is, in practice, difficult to discover. Usually, one needs to construct graph empirically according to various motivations, priors, or assumptions; this is inde- pendent of the subsequent DR mapping calculation. Different from the previous works, in this paper, we attempt to learn a graph closely linked with the DR process, and propose an al- gorithm called dimensionality reduction with adaptive graph (DRAG), whose idea is to, during seeking projection matrix, simultaneously learn a graph in the neighborhood of a pre- specified one. Moreover, the pre-specified graph is treated as a noisy observation of the ideal one, and the square Frobenius divergence is used to measure their difference in the objective function. As a result, we achieve an elegant graph update for- mula which naturally fuses the original and transformed data information. In particular, the optimal graph is shown to be a weighted sum of the pre-defined graph in the original space and a new graph depending on transformed space. Empirical results on several face datasets demonstrate the effectiveness of the proposed algorithm.
基金supported by Ministry of Higher Education MalaysiaUniversiti Teknologi MARA,Malaysia
文摘Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features' contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis (ICA) features. These local features are acquired using locally lateral subspace (LLS) strategy. Then, through linear discriminant analysis (LDA) we investigate the intraclass and interclass representation of each local ICA feature and express each feature's contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding (LLE) along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE, FERET and CK% is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects.
文摘Marginal Fisher analysis (MFA) is a repre- sentative margin-based learning algorithm for face recognition. A major problem in MFA is how to select appropriate parameters, k1 and k2, to construct the respective intrinsic and penalty graphs. In this paper, we propose a novel method called nearest-neighbor (NN) classifier motivated marginal discriminant projections (NN-MDP). Motivated by the NN classifier, NN-MDP seeks a few projection vectors to prevent data samples from being wrongly categorized. Like MFA, NN-MDP can characterize the compactness and separability of samples simultaneously. Moreover, in contrast to MFA, NN-MDP can actively construct the intrinsic graph and penalty graph without unknown parameters. Experimental results on the 0RL, Yale, and FERET face databases show that NN-MDP not only avoids the intractability, and high expense of neighborhood parameter selection, but is also more applicable to face recognition with NN classifier than other methods.
基金supported by the National Natural Science Foundation of China under Grant No. 60973070
文摘An algorithm for face description and recognition based on multi-resolution with multi-scale local binary pattern (multi-LBP) features is proposed. The facial image pyramid is constructed and each facial image is divided into various regions from which partial and holistic local binary patter (LBP) histograms are extracted. All LBP features of each image are concatenated to a single LBP eigenvector with different resolutions. The dimensionaUty of LBP features is then reduced by a local margin alignment (LMA) algorithm based on manifold, which can preserve the between-class variance. Support vector machine (SVM) is applied to classify facial images. Extensive experiments on ORL and CMU face databases clearly show the superiority of the proposed scheme over some existed algorithms, especially on the robustness of the method against different facial expressions and postures of the subjects.
基金supported by the National Natural Science Foundation of China (61071167)the Natural Science Foundation of Jiangsu Province (BK2008075),and the Natural Science Foundation for Colleges and Universities in Jiangsu Province (08KJB510016)
文摘Isometric projection (IsoProjection) is a linear dimensionality reduction method, which explicitly takes into account the manifold structure embedded in the data. However, IsoProjection is non-orthogonal, which makes it extremely sensitive to the dimensions of reduced space and difficult to estimate the intrinsic dimensionality. The non-ortbogonality also distorts the metric structure embedded in the data. This paper proposes a new method called orthogonal isometric projection (O-IsoProjection), which shares the same linear character as IsoProjection and overcomes the metric distortion problem of IsoProjection. Similar to IsoProjection, O-lsoProjection firstly constructs an adjacency graph which can reflect the manifold structure embedded in the data and the class relationship between the sample points of face space, and then obtains the projections by preserving such a graph structure. Different from IsoProjection, O-IsoProjection requires the basis vectors to be orthogonal, and the orthogonal basis vectors can be calculated by iterative way. Experimental results on ORL and Yale databases show that O-lsoProjection has better recognition rate for face recognition than Eigenface, Fisherface and IsoProjection.
文摘子空间特征提取是人脸识别中的关键技术之一,结合局部Fisher判别分析技术和张量子空间分析技术的优点,本文提出了一种新的张量局部Fisher判别分析(Tensor local Fisher discriminant analysis,TLFDA)子空间降维技术.首先,通过对局部Fisher判别技术进行分析,调整了其类间散度目标泛函,使算法的识别性能更高且时间复杂度更低;其次,引入张量型降维技术对输入数据进行双边投影变换而非单边投影,获得了更高的数据压缩率;最后,采用迭代更新的方法计算最优的变换矩阵.通过ORL和PIE两个人脸库验证了所提算法的有效性.