Eigenfaces for face detection: A novel study

Salaheddin Alakkari; John James Collins

doi:10.1109/ICMLA.2013.63

Eigenfaces for face detection: A novel study

Salaheddin Alakkari, John James Collins

Department of Computer Science & Information Systems

University of Limerick

Research output: Contribution to conference › Paper › peer-review

Abstract

A study is presented on face detection using Principal Component Analysis as a paradigm for generating compact representation for the human face. The study will focus on the contribution of individual eigenfaces in the face-space for classification in order to extract a minimum encoding for very low resolution images. The fourth, sixth, and seventh eigenfaces are identified as being particularly critical for classification, with the lowest order eigenface having a significant discriminatory contribution.

Original language	English
Pages	309-314
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLA.2013.63
Publication status	Published - 2013
Event	2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - Miami, FL, United States Duration: 4 Dec 2013 → 7 Dec 2013

Conference

Conference	2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
Country/Territory	United States
City	Miami, FL
Period	4/12/13 → 7/12/13

Keywords

classification
eigenfaces
face detection
last eigenface
Principal Component Analysis

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10.1109/ICMLA.2013.63

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title = "Eigenfaces for face detection: A novel study",

abstract = "A study is presented on face detection using Principal Component Analysis as a paradigm for generating compact representation for the human face. The study will focus on the contribution of individual eigenfaces in the face-space for classification in order to extract a minimum encoding for very low resolution images. The fourth, sixth, and seventh eigenfaces are identified as being particularly critical for classification, with the lowest order eigenface having a significant discriminatory contribution.",

keywords = "classification, eigenfaces, face detection, last eigenface, Principal Component Analysis",

author = "Salaheddin Alakkari and Collins, {John James}",

year = "2013",

doi = "10.1109/ICMLA.2013.63",

language = "English",

pages = "309--314",

note = "2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 ; Conference date: 04-12-2013 Through 07-12-2013",

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T2 - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013

AU - Alakkari, Salaheddin

AU - Collins, John James

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Y1 - 2013

N2 - A study is presented on face detection using Principal Component Analysis as a paradigm for generating compact representation for the human face. The study will focus on the contribution of individual eigenfaces in the face-space for classification in order to extract a minimum encoding for very low resolution images. The fourth, sixth, and seventh eigenfaces are identified as being particularly critical for classification, with the lowest order eigenface having a significant discriminatory contribution.

AB - A study is presented on face detection using Principal Component Analysis as a paradigm for generating compact representation for the human face. The study will focus on the contribution of individual eigenfaces in the face-space for classification in order to extract a minimum encoding for very low resolution images. The fourth, sixth, and seventh eigenfaces are identified as being particularly critical for classification, with the lowest order eigenface having a significant discriminatory contribution.

KW - classification

KW - eigenfaces

KW - face detection

KW - last eigenface

KW - Principal Component Analysis

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U2 - 10.1109/ICMLA.2013.63

DO - 10.1109/ICMLA.2013.63

M3 - Paper

AN - SCOPUS:84899432146

SP - 309

EP - 314

Y2 - 4 December 2013 through 7 December 2013

ER -

Eigenfaces for face detection: A novel study

Abstract

Conference

Keywords

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