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Facial Recognition understanding and Differences Between PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis)
2017
IJARCCE
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Face recognition technology is the end intrusive and fastest bio-metric technology to identify the human face. ...
LDA (Linear Discriminant Analysis). ...
They both are dimensionality reduction technique used for facial recognition. A. PCA-Principal Component Analysis Principal component analysis (PCA) is a dimensionality reduction technique. ...
doi:10.17148/ijarcce.2017.63128
fatcat:4iztuuaxhbaprlhdbjdm74d77i
A Priori Laplacian with Hamming Distance: Advanced Dimension Reduction Technique
2013
International Journal of Computer Applications
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This is achieved by using dimension reduction techniques like Principal component analysis (PCA), Linear Discriminant analysis (LDA), Laplacian faces and other modified approaches like A Priori Laplacian ...
The advantage of this is that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface ...
The main problem of face recognition is its high dimension space which is to be reduced by any dimension reduction techniques. ...
doi:10.5120/13426-1112
fatcat:xt4f3j5hyzg43kmjfbgh32lqli
Face Recognition Using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) Techniques
IJARCCE - Computer and Communication Engineering
2015
IJARCCE
IJARCCE - Computer and Communication Engineering
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Many techniques have been developed for the face recognition but in our work we just discussed two prevalent techniques PCA (Principal component analysis) and LDA (Linear Discriminant Analysis) and others ...
These techniques mostly used in face recognition. PCA based on the eigenfaces or we can say reduce dimension by using covariance matrix and LDA based on linear Discriminant or scatter matrix. ...
Taqdeer (Assistant Professor, Department of Computer Science and Engineering, Guru Nanak Dev University RC, Gurdaspur), for her guidance, encouragement and supervision during the period of this work. ...
doi:10.17148/ijarcce.2015.4373
fatcat:nmdj45c7cndxrcmpbhl234673u
Introductory Chapter: Face Recognition - Overview, Dimensionality Reduction, and Evaluation Methods
[chapter]
2016
Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods
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Other than this introductory chapter, this book has four more chapters, two chapters for dimensionality reduction and one for an overview of the face recognition systems and evaluation methods. ...
In-spite of these continuous efforts, there are still a plenty of scope for the new and additional research in the field of face recognition. ...
Image processing applications such as face recognition should focus on dimensionality reduction for better performance. ...
doi:10.5772/63995
fatcat:ojk4srxeyrf2nfxkxqvl64x22e
Sparse-Representation-Based Classification with Structure-Preserving Dimension Reduction
2014
Cognitive Computation
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MKLSRC using non linear kernel with kernel trick where L1 minimization is used to find sparse representation. ...
Different classification techniques are reviewed. The main objective of MKL-SRC is to classify images in environment having occlusion and noise. ...
Kashibai Navale College of Engineering-Pune, for their help. . ...
doi:10.1007/s12559-014-9252-5
fatcat:aikldxd35jd7zkygkhhlq5m6za
A Comparative Analysis of Selected Fisher Linear Discriminant Based Algorithms in Human Faces
2019
Journal of Advances in Mathematics and Computer Science
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the best most suitable of these variants of linear discriminant-based algorithms for face recognition systems remains a subject open for research. ...
and Multiclass Linear Discriminant Analysis (MLDA) in face recognition application for access control. ...
Hence, the need for dimensionality reduction. Dimensionality reduction plays crucial role in the face recognition problem. ...
doi:10.9734/jamcs/2019/v33i430188
fatcat:fnperhqqbneudhl3rax6q7muva
Face Recognition Using Multiple Classifiers
2006
Proceedings - International Conference on Tools with Artificial Intelligence, TAI
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In this paper, we propose a near real-time effective face recognition system for consumer applications. ...
To speed up KNN retrieval we propose a feature reduction technique using Principle component analysis (PCA) to facilitate near real time face recognition along with better accuracy. ...
Automatics Face Recognition falls under the Surveillance and Biometrics area. Our goal is to develop technologies for security applications. ...
doi:10.1109/ictai.2006.59
dblp:conf/ictai/ParveenT06
fatcat:v3uyd6errjh37o5jsbvmuahgqq
Dimensionality Reduction technique using Neural Networks – A Survey
2011
International Journal of Advanced Computer Science and Applications
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In this paper, we first survey related dimension reduction methods and then examine their capabilities for face recognition. ...
A self-organizing map (SOM) is a classical neural network method for dimensionality reduction. It comes under the unsupervised class. ...
This leads one to the methods of dimensionality reduction that allows one to represent data in lower dimension space. The steps for face recognition in [3] are as follow:
A. ...
doi:10.14569/ijacsa.2011.020405
fatcat:3vcktf65ynctxmmljkkpgphz2q
Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition
2013
Neural Networks
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High-dimensionality of data and the small sample size problem are two significant limitations for applying subspace methods which are favored by face recognition. ...
In this paper, a new linear dimension reduction method called locally uncorrelated discriminant projections (LUDP) is proposed, which addresses the two problems from a new aspect. ...
Conclusion Uncorrelated features of minimum redundancy are highly desirable in feature reduction for face recognition. ...
doi:10.1016/j.neunet.2013.01.009
pmid:23416750
fatcat:yxf3eaosevf43poufcmd4g65ze
Dimensionality Reduction and Classification through PCA and LDA
2015
International Journal of Computer Applications
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In this paper, well known techniques of Dimensionality Reduction namely Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are studied. ...
as Dimensionality Reduction. ...
Linear Discriminant Analysis (LDA) Linear Discriminant Analysis (LDA) is supervised dimensionality reduction technique based on a linear projection from the high dimensional space to a low dimensional ...
doi:10.5120/21790-5104
fatcat:eiuqguxhvze2lcv2b7wyqtvyqy
Application of Locality Preserving Projections in Face Recognition
2010
International Journal of Advanced Computer Science and Applications
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Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. ...
By advancing the feature extraction methods and dimensionality reduction techniques in the application of pattern recognition, a number of face recognition systems has been developed with distinct degrees ...
Another linear technique which is used for face recognition is Locality Preserving Projections (LPP) [3] , [4] , which finds an embedding that preserves local information, and gains a face subspace that ...
doi:10.14569/ijacsa.2010.010313
fatcat:htmxlcizmfeynoomot2iilxezu
Performance Evaluation of Kernel-Based Feature Extraction Techniques for Face Recognition System
2019
FUOYE Journal of Engineering and Technology
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Most existing face recognition systems have adopted different non-linear feature extraction techniques for face recognition but identification of the most suitable non-linear kernel variants for these ...
Analysis) for face recognition system. ...
dimensionality reduction. ...
doi:10.46792/fuoyejet.v4i1.285
fatcat:cbrv6s6sbfbv3gpflxyx6mervm
On Dimension Reduction Using Supervised Distance Preserving Projection for Face Recognition
2018
Universal Journal of Applied Mathematics
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Dimensionality reduction has become a necessity for pre-processing data, representation and classification. ...
In this article we have applied a Supervised distance preserving projection (SDPP) technique, Semidefinite Least Square SDPP (SLS-SDPP), we have proposed recently to reduce the dimension of face image ...
[2] uses PCA for dimension reduction step and LDA for the classification. ...
doi:10.13189/ujam.2018.060303
fatcat:7pzkvo36mjbfdhsofun2fmbk5e
Hybridization of Enhanced Orthogonal and Uncorrelated Locality Preserving Projection for Dimensionality Reduction: An HEOULPP Strategy
2020
International Journal of Intelligent Engineering and Systems
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A recent development of a linear dimension reduction (DR) algorithm that is often used in face recognition and other applications has been used to preserve the Locality preserving projection algorithm ...
Our proposed HEOULPP techniques achieves higher facial recognition rate under three conditions such as occlusion by 70.6%, noise by 69.1% and original by 76.2% than the existing techniques. ...
Two widely-used techniques for dimensional reduction include principle component analytical (PCA) and linear discriminant analysis (LDA) [9] . ...
doi:10.22266/ijies2020.0630.05
fatcat:g3yypjx42nfglkhj2jex7cnhhe
A Discriminative Non-linear Manifold Learning Technique for Face Recognition
[chapter]
2011
Communications in Computer and Information Science
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In this paper we propose a novel non-linear discriminative analysis technique for manifold learning. ...
The proposed approach is a discriminant version of Laplacian Eigenmaps which takes into account the class label information in order to guide the procedure of non-linear dimensionality reduction. ...
Introduction In recent years, a new family of non-linear dimensionality reduction techniques for manifold learning has emerged. ...
doi:10.1007/978-3-642-25483-3_28
fatcat:nd7hksqlofdaph75bgpnbqipzi
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