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An improvement to linear regression classification for face recognition

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第9期10卷 2229-2243页

作者： Peng, Yali Ke, Jingcheng Liu, Shigang Li, Jun Lei, Tao Key Lab Modern Teaching Technol Minist Educ Xian 710062 Shaanxi Peoples R China Engn Lab Teaching Informat Technol Shaanxi Prov Xian 710119 Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Shaanxi Peoples R China Southeast Univ Nanjing Sch Automat Nanjing 210096 Jiangsu Peoples R China Shaanxi Univ Sci & Technol Coll Elect & Informat Engn Xian 710021 Shaanxi Peoples R China

linear regression classification (LRC) has attracted a great amount of attention owning to its promising performance in face recognition. However, its performance will dramatically decline in the scenario of limited training samples per class, particularly when only single training sample is available for a specific person. In this paper, a novel LRC based method is proposed to solve this problem. Specifically, we first perform LRC on the training set to obtain a kind of residual for each class. Next, a reverse representation residual is derived for each training sample of a class by exploiting the linear combination of the training samples of its nearest classes and the test sample. Then, we combine the reverse representation residuals of the class by an adaptive weighted-average approach to produce the other kind of residual. Finally, two kinds of residuals are fused to classify the test sample. Experimental results on the ORL, FERET, Libor94 and CMU-PIE face databases demonstrate that the proposed method obtains a higher recognition rate than some state-of-the-art face recognition methods.

关键词： Single sample linear regression classification Adaptive weighted-average approach

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Illumination Robust Face Recognition Using Spatial Expansion Local Histogram Equalization and Locally linear regression classification 3

Illumination Robust Face Recognition Using Spatial Expansion...

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3rd International Conference on Computer and Communication Systems (ICCCS)

作者： Chang, Pei-Chun Chen, Yong-Sheng Lee, Chang-Hsing Lien, Cheng-Chang Han, Chin-Chuan Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Chung Hua Univ Dept Comp Sci & Inf Eng Hsinchu Taiwan Natl United Univ Dept Comp Sci & Inf Eng Miaoli Taiwan

ISBN: (纸本)9781538663509

Robust face recognition under illumination variations is a critical problem in a face recognition system, particularly for face recognition in the wild. In this paper, a face image preprocessing approach, called spatial expansion local histogram equalization (SELHE), is proposed to enhance face images due to illumination variations. First, a face image is divided into several non-overlapped blocks. Then, local histogram equalization with spatial expansion is proposed to enhance the contrast of each local image block. Local linear regression classification will then be used to recognize the enhanced image blocks. Experiments performed on the Yale B and Yale B extended databases have shown that the proposed approach yields promising recognition accuracy.

关键词： face recognition histogram equalization linear regression classification

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Partially-Occluded Face Recognition Using Weighted Module linear regression classification

Partially-Occluded Face Recognition Using Weighted Module Li...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chou, Yang-Ting Yang, Jar-Ferr Natl Cheng Kung Univ Dept Elect Engn Inst Comp & Commun Engn Tainan Taiwan

ISBN: (纸本)9781479953417

Accuracy and speed of face recognition frameworks are two foremost concerns for practical applications in recent researches. linear regression classification (LRC) is a very famous and powerful approach for face recognition;however, it cannot perform very well under occlusion situations. In this paper, the regression parameters of the module-LRC are analyzed when a query facial image is partially occluded. For removing contaminated modules, the weighted module linear regression classification (WMLRC) is proposed. In order to evaluate the effectiveness, AR face database is used to validate the proposed WMLRC as well as the well-known face recognition methods. Simulation results show that the proposed WMLRC method achieves the best performance for partially-occluded faces while keeping the advantage in speed over the SRC-based approaches.

关键词： face recognition linear regression classification AR face database

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Partially-occluded face recognition using weighted module linear regression classification

Partially-occluded face recognition using weighted module li...

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International Symposium on Circuits and Systems

作者： Yang-Ting Chou Jar-Ferr Yang Institute of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781479953424

关键词： AR face database face recognition linear regression classification

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Double linear regression classification for face recognition

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JOURNAL OF MODERN OPTICS 2015年第4期62卷 288-295页

作者： Feng, Qingxiang Zhu, Qi Tang, Lin-Lin Pan, Jeng-Shyang Harbin Inst Technol Shenzhen Grad Sch Shenzhen Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China

A new classifier designed based on linear regression classification (LRC) classifier and simple-fast representation-based classifier (SFR), named double linear regression classification (DLRC) classifier, is proposed for image recognition in this paper. As we all know, the traditional LRC classifier only uses the distance between test image vectors and predicted image vectors of the class subspace for classification. And the SFR classifier uses the test image vectors and the nearest image vectors of the class subspace to classify the test sample. However, the DLRC classifier computes out the predicted image vectors of each class subspace and uses all the predicted vectors to construct a novel robust global space. Then, the DLRC utilizes the novel global space to get the novel predicted vectors of each class for classification. A mass number of experiments on AR face database, JAFFE face database, Yale face database, Extended YaleB face database, and PIE face database are used to evaluate the performance of the proposed classifier. The experimental results show that the proposed classifier achieves better recognition rate than the LRC classifier, SFR classifier, and several other classifiers.

关键词： face image representation and recognition linear regression classification nearest neighbor classifier simple-fast representation-based classifier

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Effective face recognition using dual linear collaborative discriminant regression classification algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第5期81卷 6899-6922页

作者： Hosgurmath, Sangamesh Mallappa, Viswanatha Vanjre Patil, Nagaraj B. Petli, Vishwanath Visvesvaraya Technol Univ Dept Elect & Commun Engn Belagavi India HEK Soc SLN Coll Engn Dept Elect & Commun Engn Raichur India Govt Engn Coll Dept Comp Sci & Engn Gangavathi Karnataka India

In recent decades, face recognition is an attractive and emerging research area in computer vision and pattern recognition applications. Still, facial recognition is a challenging task due to the following factors;different face expressions, posture, wearing of scarves/glasses, and illumination conditions. To overcome the aforementioned factors, a new Dual linear Collaborative Discriminant regression classification (DLCDRC) algorithm is introduced in this research paper. The proposed DLCDRC algorithm contains dual steps such as deep loss function, and combined distance metric. The deep loss function includes inter-class and intraclass loss functions, which are used to decrease inter and intraclass variations in Within Class Reconstruction Error (WCRE), and Collaborative between Class Reconstruction Error (CBCRE). Additionally, the combined distance metric is used to construct CBCRE and WCRE with minimum reconstruction error values that efficiently improve the face recognition performance. By inspecting the resulting phase, the DLCDRC algorithm achieved 92.47%, 96.39%, and 98.86% of classification accuracy on YALE B, ORL, and extended YALE B face datasets. The proposed DLCDRC method has the improvement of 1.85% in Extended YALE B dataset, 3.05% improvement in ORL dataset, and improvement of 2.4% in YALE B dataset. The obtained simulation results are better compared to the existing linear regression algorithms on three benchmark datasets.

关键词： Combined distance metric Deep loss function Face recognition linear discriminant analysis linear regression classification Projection matrix

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Combination of linear regression classification and collaborative representation classification

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NEURAL COMPUTING & APPLICATIONS 2014年第3-4期25卷 833-838页

作者： Zhang, Hongzhi Wang, Faqiang Chen, Yan Zhang, Dapeng Wang, Kuanquan Liu, Jingdong Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China Northeast Forestry Univ Coll Informat & Comp Engn Harbin Peoples R China Harbin Vicog Intelligent Syst Co Ltd Harbin Peoples R China

classification using the l (2)-norm-based representation is usually computationally efficient and is able to obtain high accuracy in the recognition of faces. Among l (2)-norm-based representation methods, linear regression classification (LRC) and collaborative representation classification (CRC) have been widely used. LRC and CRC produce residuals in very different ways, but they both use residuals to perform classification. Therefore, by combining the residuals of these two methods, better performance for face recognition can be achieved. In this paper, a simple weighted sum based fusion scheme is proposed to integrate LRC and CRC for more accurate recognition of faces. The rationale of the proposed method is analyzed. Face recognition experiments illustrate that the proposed method outperforms LRC and CRC.

关键词： linear regression classification Collaborative representation classification Sparse representation classification Weighted sum based fusion scheme Face recognition

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Application of linear regression classification to low-dimensional datasets

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NEUROCOMPUTING 2014年 131卷 331-335页

作者： Koc, Mehmet Barkana, Atalay Bilecik Seyh Edebali Univ Dept Elect & Elect Engn TR-11210 Bilecik Turkey Anadolu Univ Dept Elect & Elect Engn TR-26555 Eskisehir Turkey

The Traditional linear regression classification (LRC) method fails when the number of data in the training set is greater than their dimensions. In this work, we proposed a new implementation of LRC to overcome this problem in the pattern recognition. The new form of LRC works even in the case of having low-dimensional excessive number of data. In order to explain the new form of LRC, the relation between the predictor and the correlation matrix of a class is shown first. Then for the derivation of LRC, the null space of the correlation matrix is generated by using the eigenvectors corresponding to the smallest eigenvalues. These eigenvectors are used to calculate the projection matrix in LRC. Also the equivalence of LRC and the method called Class-Featuring Information Compression (CLAFIC) is shown theoretically. TI Digit database and Multiple Feature dataset are used to illustrate the use of proposed improvement on LRC and CLAFIC. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Correlation matrix Subspace methods linear regression classification Class-featuring information compression

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classification by Principal Component regression in the Real and Hypercomplex Domains

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第8期48卷 10099-10108页

作者： El-Melegy, Moumen T. Kamal, Aliaa T. Hussain, Khaled F. El-Hawary, H. M. Assiut Univ Fac Engn Elect Engn Dept Assiut Egypt Assiut Univ Fac Comp & Informat Comp Sci Dept Assiut Egypt Assiut Univ Fac Sci Math Dept Assiut Egypt

linear regression is a simple and widely used machine learning algorithm. It is a statistical approach for modeling the relationship between a scalar variable and one or more variables. In this paper, a classification by principal component regression (CbPCR) strategy is proposed. This strategy depends on performing regression of each data class in terms of its principal components. This CbPCR formulation leads to a new formulation of the linear regression classification (LRC) problem that preserves the key information of the data classes while providing more compact closed-form solutions. For the sake of image classification, this strategy is also extended to the 4D hypercomplex domains to take into account the color information of the image. Quaternion and reduced biquaternion CbPCR strategies are proposed by representing each channel of the color image as one of the imaginary parts of a quaternion or reduced biquaternion number. Experiments on two color face recognition benchmark databases show that the proposed methods achieve better accuracies by a margin of about 3% over the original LRC and like methods.

关键词： linear regression classification Principal component analysis Hypercomplex numbers Face recognition

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A strategy to significantly improve the classification accuracy of LIBS data:application for the determination of heavy metals in Tegillarca granosa

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Plasma Science and Technology 2021年第8期23卷 118-126页

作者： Yangli XU Liuwei MENG Xiaojing CHEN Xi CHEN Laijin SU Leiming YUAN Wen SHI Guangzao HUANG Wenzhou Vocational College of Science and Technology Wenzhou 325006People's Republic of China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou 325035People's Republic of China Research and Development Department Hangzhou Goodhere Biotechnology Co.LtdHangzhou 311100People's Republic of China College of Life and Environmental Science Wenzhou UniversityWenzhou 325035People's Republic of China

Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy ***,it is essential to develop a rapid detection method for Tegillarca *** this issue,five categories of Tegillarca granosa samples consisting of a healthy group;Zn,Pb,and Cd polluted groups;and a mixed pollution group of all three metals were used to detect heavy metal pollution by combining laser-induced breakdown spectrometry(LIBS)and the newly proposed linear regression classification-sum of rank difference(LRC-SRD)*** the comparison models,least regression classification(LRC),support vector machine(SVM),and k-nearest neighbor(KNN)and linear discriminant analysis were also *** accuracy(0.93)was obtained by LRC-SRD model and which performs better than other *** demonstrated that LIBS coupled with LRC-SRD is an efficient framework for Tegillarca granosa heavy metal detection and provides an alternative to replace traditional methods.

关键词： Tegillarca granosa sum of ranking difference heavy metal linear regression classification

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