The LDA-Based Face Recognition at a Distance Using Multiple Distance Image

Moon, Hyung Nam; Pan, Sung Bum

doi:10.1109/imis.2013.50

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Eigen Faces Method: This schema decomposes images into a small set of characteristic features called 'Eigen Faces' which is based on the principal [5,6] component of the initial set of face images. Any face can be expressed [3], as linear combinations of the singular vectors of the set of faces, and these singular vectors are eigenvectors of the covariance matrices.…”

Section: Existing Systemmentioning

confidence: 99%

Face Recognition by Classification using Radial Basis Function

Mandhala¹,

Bhattacharyya

Kim

2015

IJMUE

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The face recognition task involves extraction of unique features from the human face. Manifold learning methods are proposed to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. PCA and LDA are used for the feature extraction and the resultant feature vectors are fused with the different fusion techniques and the proposed method yields good recognition rate with PCA Fusion of PCA and LDA features and those are classified with neural network. In general the size of the face database is too high and it needs more memory and needs more time for training so that to improve time and space complexities there is a need for dimensionality reduction. The extracted features are classified with Neural Network to improve the recognition rate.

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Section: Existing Systemmentioning

confidence: 99%

Face Recognition by Classification using Radial Basis Function

Mandhala¹,

Bhattacharyya

Kim

2015

IJMUE

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“…To emphasize a feasibility of this technique, in 2003, Draper et al [ 15 ] compared Manhattan distance, ED, and Mahalanobis distance over PCA and then found out that ED have the highest accuracy rate. In addition, recently, in 2013, Moon and Pan [ 42 ] compared Manhattan distance, ED, cosine similarity, and Mahalanobis distance over LDA face recognition in which the results lead to an outstanding performance of ED. In addition, to further speed up the computational time, Li et al [ 43 ] proposed ED for matrix calculation on a large dataset using multiprocessing, which applied a traditional parallel matrix operation.…”

Section: Related Workmentioning

confidence: 99%

PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

Rujirakul

So-In

Arnonkijpanich

2014

The Scientific World Journal

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Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

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Using Contourlet Transform Based RBFN Classifier for Face Detection and Recognition

Vinothkanna

Vijayakumar

2019

Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

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The LDA-Based Face Recognition at a Distance Using Multiple Distance Image

Cited by 7 publications

References 10 publications

Face Recognition by Classification using Radial Basis Function

Face Recognition by Classification using Radial Basis Function

PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

Using Contourlet Transform Based RBFN Classifier for Face Detection and Recognition

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