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Face Recognition Using Classification-Based Linear Projections
2008
EURASIP Journal on Advances in Signal Processing
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Subspace methods have been successfully applied to face recognition tasks. In this study we propose a face recognition algorithm based on a linear subspace projection. ...
Unlike previously suggested supervised subspace methods, the algorithm explicitly utilizes the classification performance criterion to obtain the optimal linear projection. ...
The ICA selects a linear projection that maximizes the degree of statistical independence of output variables based on various contrast functions (see [10] for an application of ICA to face recognition ...
doi:10.1155/2008/416318
fatcat:avd2ii5ckjacnpfquf6thzlgum
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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One of most the important subtopics in face recognition is dimensionality reduction [1], because storing and processing of these high-resolution face images from huge database using light-weight devices ...
These face recognition systems provide better performance in one aspect and lack in other aspect. Comprehensive evaluation the performances of face recognition systems is the need of the hour. ...
Subspace projection techniques are highly useful and classical option in face recognition is useful for reducing the dimension. ...
doi:10.5772/63995
fatcat:ojk4srxeyrf2nfxkxqvl64x22e
PCA and LDA Based Face Recognition Using Feedforward Neural Network Classifier
[chapter]
2006
Lecture Notes in Computer Science
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Principal component analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are among the most common feature extraction techniques used for the recognition of faces. ...
The proposed systems show improvement on the recognition rates over the conventional LDA and PCA face recognition systems that use Euclidean Distance based classifier. ...
Classification is performed by comparing the projection vectors of the training face images with the projection vector of the input face image based on the Euclidean Distance between the faces classes ...
doi:10.1007/11848035_28
fatcat:jlx4scrnm5c33jmqjlabgb2ez4
Manifold Modeling with Learned Distance in Random Projection Space for Face Recognition
2010
2010 20th International Conference on Pattern Recognition
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We demonstrate that this approach is effective for multi view face recognition. ...
In the proposed system, initial dimensionality reduction is achieved using random projections, a computationally efficient and data independent linear transformation. ...
The method achieved higher recognition accuracy compared to Eigenfaces [16] (PCA based) and Fisherfaces [17] (discriminant based) that model faces using linear structures. ...
doi:10.1109/icpr.2010.165
dblp:conf/icpr/TsagkatakisS10
fatcat:zlixruu6ijbl7bmd2lzqi2ijzu
The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance
[chapter]
2012
Principal Component Analysis
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Using 1 Smile Stage Classification Recognition Rate Based on the Maximum Value Selection of Kernel Linear Preserving Projection Method Using 2 Smile Stage Classification Recognition Rate Based on the ...
Experimental results of smile stage classification based on the maximum value selection of kernel linear preserving projection To evaluate the Maximum Value Selection of Kernel Linear Preserving Projection ...
In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. ...
doi:10.5772/38226
fatcat:ytw5qta67vht5lzt7n5ylzhfh4
Expression Recognition Using RP and DPL
2017
DEStech Transactions on Engineering and Technology Research
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Using different Random Projection (RP) for each class features and Dictionary Pairs Learning (DPL) classification algorithm, the proposed approach of face expression recognition can classify the prototypic ...
emotional facial expressions with improved computation burden and recognition performance. ...
also used in the face expressions recognition [3] . ...
doi:10.12783/dtetr/apetc2017/11380
fatcat:7c4i6wifcvfejapycnnpwzklyi
Shared Feature Extraction for Nearest Neighbor Face Recognition
2008
IEEE Transactions on Neural Networks
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The problem of finding the optimal linear projection matrix is defined as a classification problem and the Adaboost algorithm is used to compute it in an iterative way. ...
Index Terms-Face recognition, feature extraction, multitask learning (MTL), nearest neighbor classification (NN), small sample size problem. ...
A linear projection matrix is obtained selecting 1-D feature extractors shared among the different classification problems, using the multiclass extension of the Adaboost-based feature extraction algorithm ...
doi:10.1109/tnn.2007.911742
pmid:18390306
fatcat:py7ms2ldkrfw7i76742x352use
Advances of Robust Subspace Face Recognition
[chapter]
2016
Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods
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Over past years, subspace projection methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), are the well-known algorithms for face recognition. ...
Recently, linear regression classification (LRC) is one of the most popular approaches through subspace projection optimizations. ...
Moreover, the robust linear regression classification (RLRC) [9] estimating regression parameters by using the robust Huber estimation was introduced to achieve robust face recognition under illumination ...
doi:10.5772/62735
fatcat:uu7rbrdwy5e3vmssnea3epqhjy
SVM-based Multiview Face Recognition by Generalization of Discriminant Analysis
[article]
2010
arXiv
pre-print
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Multiview faces are having difficulties due to non-linear representation in the feature space. ...
This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. ...
For robust and efficient classification of face images, the Gabor filter responses project onto another sub-space using canonical covariate based on the principal axis in terms of linear features. ...
arXiv:1001.4140v1
fatcat:sav3lhyqzzgx3deufgxwbb3tgu
Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition
[chapter]
2008
Recent Advances in Face Recognition
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face recognition. ...
Linear-based suspace analysis Subspace analysis methods are the processes of projecting high dimensional data to a lower dimensional subspace which are used for visualization or dimensionality reduction ...
doi:10.5772/6400
fatcat:ibwdm3ybuvcovafszx5n7dtagy
Class-specific kernel linear regression classification for face recognition under low-resolution and illumination variation conditions
2016
EURASIP Journal on Advances in Signal Processing
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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. ...
With the proposed class-specific kernel projection combined with linear regression classification, the class label can be determined by calculating the minimum projection error. ...
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
Face Recognition using R-KDA with Non-Linear SVM for Multi-View Database
2015
Procedia Computer Science
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This paper develops a new Face Recognition System which combines R-KDA for selecting optimal discriminant features with non-linear SVM for Recognition. ...
SVM has been used in classification in many face recognition systems. In our Face Recognition System, R-KDA 13 is used for feature extraction and non-linear SVM, for classification. ...
., 14 proposed a new PCA based face recognition method in which robust facial features are represented using Gabor features, which are again transformed into Eigenspace using PCA for classification. ...
doi:10.1016/j.procs.2015.06.061
fatcat:z3jsvbsgdjfztcpx5zp62p2jcy
Svm-Based Multiview Face Recognition By Generalization Of Discriminant Analysis
2008
Zenodo
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Multiview faces are having difficulties due to non-linear representation in the feature space. ...
This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. ...
For robust and efficient classification of face images, the Gabor filter responses project onto another sub-space using canonical covariate based on the principal axis in terms of linear features. ...
doi:10.5281/zenodo.1063333
fatcat:sapkkhyno5cfjkqksls6oylx5y
Person Recognition by Hilbert Pair of Wavelets using Facial Images
2018
International Journal of Engineering & Technology
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Results show that proposed DTMBWT based face recognition provides better results than other approaches. ...
Though there are many types of face detection/recognition system found no method can give the 100% accurate outputs. ...
Face recognition technique based on a category particular dictionary, and a projection matrix is discussed by Cao et. al. [4] . ...
doi:10.14419/ijet.v7i3.11482
fatcat:uajx2fl3hzcerputhawpdtvlcu
A near optimal projection for Sparse representation based classification
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
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Sparse representation based classification (SRC) is one of the most successful methods that has been developed in recent times for face recognition. ...
Here, we propose a new projection technique using the data scatter matrix which is computationally superior to the optimal projection method with comparable classification accuracy with respect OPSRC. ...
CONCLUSION Sparse representation based classification for face recognition has proven to outperform conventional face recognition techniques. ...
doi:10.1109/icassp.2013.6638022
dblp:conf/icassp/RajaB13
fatcat:o6c3jujt2fh5zpghtop3sasueu
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