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Kernel linear regression for low resolution face recognition under variable illumination

Shih-Ming Huang, Jar-Ferr Yang
2012 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
To improve the limitation of linear regression classification, a class specific kernel linear regression classification is proposed for low resolution face recognition under variable illumination.  ...  The nonlinear mapping function enhances the modeling capability for highly nonlinear data distribution.  ...  Recently, a linear regression classification (LRC) algorithm [13] has been proposed for face recognition, which is based on that face images from a specific class are known to lie on a linear subspace  ... 
doi:10.1109/icassp.2012.6288286 dblp:conf/icassp/HuangY12 fatcat:tmnaamdksjg23mwp4fjl2e5enu

Secure Data Access Computing Model on Mobile Cloud Data using Fusion of Finger Print and Face biometric authentication based on Discrete Combinational models

A.Amali Mary Bastina
2021 Bioscience Biotechnology Research Communications  
In this paper, we propose a combinational model composed differential evolution technique to enhance the recognition of finger print and face patterns to authenticate the user towards data access.  ...  Multimodal authentication performed using Multivariate linear regression on less no of optimal features.  ...  Finally multivariate linear regression classifier is used for feature classification or recognition.  ... 
doi:10.21786/bbrc/14.7.64 fatcat:p5hrm7lgbffepphclunz3zvrde

Fusion of Logically Concatenated Cross Binary Pattern and ε-Dragging Linear Regression for Face Classification across Poses

Kumud Arora, Inderprastha Engineering College, Ghaziabad, India, Poonam Garg
2020 International Journal of Machine Learning and Computing  
Index Terms-Discriminative elastic-net regularization, logically concatenated cross binary pattern, marginalized elastic-net regularized linear regression, negative dragging linear regression.  ...  Classification is realized using regression residuals in the least-squares sense.  ...  Feature extraction using the LC-CBP descriptor. (3) Face classification using the ε-dragging approach for discriminative linear regression.  ... 
doi:10.18178/ijmlc.2020.10.1.906 fatcat:fvebswlg4ndhtmu6q7bw7dgena

A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

Sangamesh Hosgurmath, Viswanatha Vanjre Mallappa, Nagaraj B. Patil, Vishwanath Petli
2022 International Journal of Power Electronics and Drive Systems (IJPEDS)  
The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition.  ...  Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing.  ...  Classification using linear collaborative discriminant regression classification Using linear collaborative discriminant regression classification (LCDRC), classification is carried out after extracting  ... 
doi:10.11591/ijece.v12i2.pp1468-1476 fatcat:ostrn462ivhvzgziqkns4awwhu

Class-specific kernel linear regression classification for face recognition under low-resolution and illumination variation conditions

Yang-Ting Chou, Shih-Ming Huang, Jar-Ferr Yang
2016 EURASIP Journal on Advances in Signal Processing  
In this paper, a novel class-specific kernel linear regression classification is proposed for face recognition under very low-resolution and severe illumination variation conditions.  ...  linear regression for the ill-posed data distribution.  ...  Recently, the spare representation classification (SRC) [17, 18] and a linear regression classification (LRC) algorithms [19] have been proposed for face recognition.  ... 
doi:10.1186/s13634-016-0328-0 fatcat:p6y2hdiouffqbcwojxob4kiikm

Noisy label based discriminative least squares regression and its kernel extension for object identification

2017 KSII Transactions on Internet and Information Systems  
Inspired by discriminative least squares regression algorithm (DLSR), a noisy label based discriminative least squares regression algorithm (NLDLSR) is presented in this paper.  ...  In addition, the proposed NLDLSR is expanded to the kernel space, and we further propose a novel kernel noisy label based discriminative least squares regression algorithm (KNLDLSR).  ...  [26] proposed a general regression and representation (GRR) algorithm for image recognition and classification.  ... 
doi:10.3837/tiis.2017.05.012 fatcat:dqfbwf3uvbcmzig3vbmyp4ga6q

Bilateral Two-Dimensional Matrix Regression Preserving Discriminant Embedding for Corrupted Image Recognition

Jianbo Zhang, Jinkuan Wang, Mingwei Li
2019 IEEE Access  
Hence, B2DMRPDE can capture the potential discriminative information for classification.  ...  INDEX TERMS Corrupted image, face recognition, low-rank, matrix regression, nuclear-norm.  ...  ACKNOWLEDGMENT The authors would like to thank the online providers of databases containing faces, including Yale, Extended Yale B, CMU-PIE, AR, and LFW.  ... 
doi:10.1109/access.2019.2892955 fatcat:jv72a4cq5vdkznkvwgm6nlug4m

Discriminative Elastic-Net Regularized Linear Regression

Zheng Zhang, Zhihui Lai, Yong Xu, Ling Shao, Jian Wu, Guo-Sen Xie
2017 IEEE Transactions on Image Processing  
Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification.  ...  In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems.  ...  Face Recognition Evaluation In this section, we evaluate the performances of our method for face recognition on four face databases.  ... 
doi:10.1109/tip.2017.2651396 pmid:28092552 fatcat:lb3ljuqwwrfl3o3c27pkii6yvi

A Novel Mechanism of Face Recognition Using Stepwise Linear Discriminant Analysis and Linear Vector Quantization Classifiers

Abdul Quyoom
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
Here stepwise linear discriminant analysis (SWLDA) is used for the feature extraction and Linear Vector Quantization (LVQ) Classifier is used for face recognition.  ...  In recognition, firstly face is detected using canny edge detection method, after face detection SWLDA is employed to extract the face features, and end linear vector quantization is applied for face recognition  ...  The performance of geometry based recognition methods entirely depends on the facial landmark extraction accuracy. A. Classification Various methods have been proposed for face classification.  ... 
doi:10.23956/ijarcsse.v7i7.96 fatcat:vhl3pprqhjduvkv5gayyftpv5q

Facial Expression Identification System Using fisher linear discriminant analysis and K- Nearest Neighbor Methods

2019 Zanco Journal of Pure and Applied Sciences  
A facial expressions recognition system using each of FLDA with K-nearest neighbors (K-NN) classifier is introduced in this research.  ...  The system is applied to recognize various basic facial expressions such as happy, neutral, angry, disgust, sad, fear and surprise, in the Karolinska Directed Emotional Faces (KDEF) and Japanese Female  ...  K-Nearest Neighbor It is one of the famous and simple methods that is applied for classification and regression.  ... 
doi:10.21271/zjpas.31.2.2 fatcat:bauj5ykrdjcvdj4anku47tlnpe

Face Recognition across Pose using ELM Framework

Kumud Arora, Poonam Garg, Virendra P. Vishwakarma
2018 European Journal of Electrical Engineering and Computer Science  
This paper proposes the use of Extreme Learning Machine regression and classification framework to recognize face across pose.  ...  Kernel version of ELM is used for the non-linear mapping estimation between frontal and its corresponding non frontal view.  ...  Non-linear regression via kernel ELM is proposed to estimate the non-linear mapping between frontal face views from its counter-part non-frontal views for effective face recognition.  ... 
doi:10.24018/ejece.2018.2.3.23 fatcat:7oaqhtqfx5bothukhu7d36wjba

Class-Specific Nonlinear Subspace Learning Based On Optimized Class Representation

Alexandros Iosifidis, I. Pitas, Anastasios Tefas
2015 Zenodo  
Table 1 . 1 Performance for different training percentage on the face recognition datasets.  ...  INTRODUCTION Standard Discriminant Learning techniques, like Linear Discriminant Analysis (LDA) [1, 2] , Kernel Discriminant Analysis (KDA) [3] , (kernel) Spectral Regression (KSR) [4] and Class-specific  ... 
doi:10.5281/zenodo.54342 fatcat:qrpc6iowpvephhbp7ygd7bwn3y

Class-specific nonlinear subspace learning based on optimized class representation

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
Table 1 . 1 Performance for different training percentage on the face recognition datasets.  ...  INTRODUCTION Standard Discriminant Learning techniques, like Linear Discriminant Analysis (LDA) [1, 2] , Kernel Discriminant Analysis (KDA) [3] , (kernel) Spectral Regression (KSR) [4] and Class-specific  ... 
doi:10.1109/eusipco.2015.7362833 dblp:conf/eusipco/IosifidisTP15 fatcat:2sqcykwibzdp3pwcwv6uldu4ju

Face Recognition System Based on Spectral Graph Wavelet Theory

R. Premalatha Kanikannan, K. Duraiswamy
2014 Research Journal of Applied Sciences Engineering and Technology  
This study presents an efficient approach for automatic face recognition based on Spectral Graph Wavelet Theory (SGWT).  ...  The given face image is decomposed by SGWT at first. The energies of obtained sub-bands are fused together and considered as feature vector for the corresponding image.  ...  A linear discriminate regression classification algorithm is implemented in Huang and Yang (2013a) to boost the effectiveness of the Linear Regression Classification (LRC) for face recognition.  ... 
doi:10.19026/rjaset.8.1121 fatcat:d3jdpgbtx5c3fby3hai7vhw5aq

An Assistive Object Recognition System for Enhancing Seniors Quality of Life

Esraa Elhariri, Nashwa El-Bendary, Aboul Ella Hassanien, Vaclav Snasel
2015 Procedia Computer Science  
Linear Discriminant Analysis (LDA) algorithms, for classifying different indoor objects to improve quality of elderly people's life.  ...  Datasets used for these experiments, are totally consisted of 347 images with different eight indoor objects used for both training and testing datasets.  ...  Discriminant Analysis (LDA): Linear Discriminant Analysis (LDA) is a commonly used technique for data classification and dimensionality reduction.  ... 
doi:10.1016/j.procs.2015.09.013 fatcat:g33zhjwy25ab5eyfr6ca2cd7be
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