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Facial Recognition understanding and Differences Between PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis)

Aarav Yadav, Ms. Anamika Jain
2017 IJARCCE  
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

Gaurav Gupta
2013 International Journal of Computer Applications  
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

Amritpal Kaur, Sarabjit Singh, Taq dir
2015 IJARCCE  
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]

S. Ramakrishnan
2016 Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods  
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

Jin Xu, Guang Yang, Yafeng Yin, Hong Man, Haibo He
2014 Cognitive Computation  
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

Oladotun O. Okediran, Temitope O. Ashaolu, Elijah O. Omidiora
2019 Journal of Advances in Mathematics and Computer Science  
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

Pallabi Parveen, Bhavani Thuraisingham
2006 Proceedings - International Conference on Tools with Artificial Intelligence, TAI  
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

Shamla Mantri, Nikhil S., Sudip C.
2011 International Journal of Advanced Computer Science and Applications  
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

Yu Chen, Wei-Shi Zheng, Xiao-Hong Xu, Jian-Huang Lai
2013 Neural Networks  
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

Telgaonkar ArchanaH., Deshmukh Sachin
2015 International Journal of Computer Applications  
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

Shermina J
2010 International Journal of Advanced Computer Science and Applications  
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

Bukola O Makinde, Olusayo D Fenwa, Adeleye S Falohun, Olufemi A Odeniyi
2019 FUOYE Journal of Engineering and Technology  
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

S. Jahan
2018 Universal Journal of Applied Mathematics  
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

Ummadi Kumar, Acharya Nagarjuna University, Edara Reddy, Acharya Nagarjuna University
2020 International Journal of Intelligent Engineering and Systems  
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]

Bogdan Raducanu, Fadi Dornaika
2011 Communications in Computer and Information Science  
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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