2D-PCA Representation and Sparse Representation for Image Recognition

2017 ◽  
Vol 14 (1) ◽  
pp. 829-834 ◽  
Author(s):  
Chunwei Tian ◽  
Qi Zhang ◽  
Jian Zhang ◽  
Guanglu Sun ◽  
Yuan Sun
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Image Classification ◽  
Principal Component ◽  
Face Image ◽  
Theoretical Research ◽  
Optimal Weights ◽  
Face Images ◽  
Pca Method ◽  
Pca Algorithm

The two-dimensional principal component analysis (2D-PCA) method has been widely applied in fields of image classification, computer vision, signal processing and pattern recognition. The 2D-PCA algorithm also has a satisfactory performance in both theoretical research and real-world applications. It not only retains main information of the original face images, but also decreases the dimension of original face images. In this paper, we integrate the 2D-PCA and spare representation classification (SRC) method to distinguish face images, which has great performance in face recognition. The novel representation of original face image obtained using 2D-PCA is complementary with original face image, so that the fusion of them can obviously improve the accuracy of face recognition. This is also attributed to the fact the features obtained using 2D-PCA are usually more robust than original face image matrices. The experiments of face recognition demonstrate that the combination of original face images and new representations of the original face images is more effective than the only original images. Especially, the simultaneous use of the 2D-PCA method and sparse representation can extremely improve accuracy in image classification. In this paper, the adaptive weighted fusion scheme automatically obtains optimal weights and it has no any parameter. The proposed method is not only simple and easy to achieve, but also obtains high accuracy in face recognition.

scholarly journals Face recognition using eigenface approach

2012 ◽  
Vol 9 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Marijeta Slavkovic ◽  
Dubravka Jevtic
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Experimental Results ◽  
Face Images ◽  
Face Recognition System ◽  
Small Set ◽  
Pca Method ◽  
Pca Algorithm

In this article, a face recognition system using the Principal Component Analysis (PCA) algorithm was implemented. The algorithm is based on an eigenfaces approach which represents a PCA method in which a small set of significant features are used to describe the variation between face images. Experimental results for different numbers of eigenfaces are shown to verify the viability of the proposed method.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

Face Recognition by PCA and Improved LBP Fusion Algorithm

2015 ◽  
Vol 734 ◽  
pp. 562-567 ◽  
Author(s):  
En Zeng Dong ◽  
Yan Hong Fu ◽  
Ji Gang Tong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Rotation Invariant ◽  
Fusion Algorithm ◽  
Face Images ◽  
The Face ◽  
Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

scholarly journals Half-face based recognition using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1404
Author(s):  
Ahmed M. Alkababji ◽  
Sara Raed Abd
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Face Images ◽  
The Face ◽  
Considerable Problem ◽  
Full Face ◽  
Pca Algorithm

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

Improved combination of RPCA and MEL for sparse representation-based face recognition

2014 ◽  
Vol 12 (03) ◽  
pp. 1450031 ◽  
Author(s):  
Rokan Khaji ◽  
Hong Li ◽  
Hongfeng Li ◽  
Rabiu Haruna ◽  
Ramadhan Abdo Musleh Alsaidi
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Sparse Representation ◽  
Principal Component ◽  
Second Phase ◽  
Robust Principal Component Analysis ◽  
Practical Applications ◽  
Face Images ◽  
Two Phases ◽  
Improved Technique

Face recognition (FR) is an important and challenging task in pattern recognition and has many important practical applications. This paper presents an improved technique for Face Recognition, which consists of two phases where in each phase; a technique is employed effectively that is used extensively in computer vision and pattern recognition. Initially, the Robust Principal Component Analysis (RPCA) is used specifically in the first phase, which is employed to reduce dimensionality and to extract abstract features of faces. The framework of the second phase is sparse representation based classification (SRC) and introduced metaface learning (MFL) of face images. Experiments for face recognition have been performed on ORL and AR face database. It is shown that the proposed method can perform much best than other methods. And with the proposed method, we can obtain a best understanding of data.

scholarly journals Face Recognition Based on Viola-Jones Face Detection Method and Principle Component Analysis (PCA)

2018 ◽  
pp. 52-59
Keyword(s):  
Face Recognition ◽  
Image Recognition ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Face Image ◽  
Important Research ◽  
Research Fields ◽  
Actual Use ◽  
Pca Method

Face recognition is one of the most important research fields of the last two decades. This is due to the actual use of this technology in automatic detection and monitoring systems. Face attribute and features recognition from images is still a challenge. In this paper, face image recognition is proposed upon local face image rather than focusing on the whole image recognition by applying preprocessing techniques and Viola-Jones method. Principal Component Analysis (PCA) method is used in order to extract the needed features. Experiments show satisfied and more accurate results achieved by the proposed system comparing to the existing systems.

Improved Principal Component Analysis for Face Image Recognition

2013 ◽  
Vol 380-384 ◽  
pp. 3734-3737
Author(s):  
Jina De Zhang ◽  
Chen Rong Hang ◽  
Jin Bao Xu ◽  
Hang Chen
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Face Image ◽  
Recognition Problem ◽  
Analysis Algorithm ◽  
Algorithm Efficiency ◽  
Face Images ◽  
New Strategy ◽  
Improve Algorithm ◽  
Detailed Computer

Principal cmponent analysis is applied to face recognition problem to shorten image processing time in this paper. Based on the analysis of basic principal cmponent analysis algorithm, a new strategy which can improve algorithm efficiency by reducing the dimension of face images to be processed is introduced to face recognition problem, and the detailed computer implementation procedure is given. Numerical experiments have been performed to evaluate the efficiency of improved principal cmponent analysis algorithm for the problem of face recognition.

scholarly journals Face Recognition on Bag Locking Mechanism

2019 ◽  
Vol 18 (2) ◽  
pp. 16-22
Author(s):  
Mohamad Hafis Izran Ishak ◽  
Nurul Hawani Idris ◽  
Shafishuhaza Sahlan
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Raspberry Pi ◽  
Theory Approach ◽  
Human Beings ◽  
Face Images ◽  
Locking Mechanism ◽  
Small Set ◽  
Pca Method ◽  
Analyse Data

With the emergent of biometric technology, people are no longer afraid to keep their important things in the safe box or room or even facility. This is because; human beings have unique features that distinguish them with other people. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called ‘Eigenfaces’, which are actually the principal components of the initial training set of face images. In this report, thorough explanation on design process of face recognition on bags locking mechanism will be elucidated. The results and analysis of the proposed design prototype also presented and explained. The platform for executing the algorithm is on the Raspberry Pi. There are two artificial intelligent techniques applied to manipulate and processing data which is fuzzy logic and neural networks. Both systems are interdependent with each other, so that it can calculate and analyse data precisely. The receive image from the camera is analysed through the Eigenfaces algorithm. The algorithm is using Principal Component Analysis (PCA) method which comprise of artificial neural network paradigm and also statistical paradigm.

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

2021 ◽  
pp. 1-15
Author(s):  
Yongjie Chu ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
High Resolution ◽  
Domain Adaptation ◽  
Face Image ◽  
Discriminative Learning ◽  
Low Resolution ◽  
Face Images ◽  
World Environment

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

scholarly journals JointL1/2-Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Chun-Mei Feng ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Juan Wang ◽  
Dong-Qin Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Error Function ◽  
Principal Component ◽  
Graph Laplacian ◽  
Specific Gene ◽  
Data Sets ◽  
Pca Method ◽  
Norm Constraint ◽  
Alm Method ◽  
Pca Algorithm

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adoptingL1/2constraint (L1/2gLPCA) on error function for feature (gene) extraction. The error function based onL1/2-norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others.

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