Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems

Sepehr Damavandinejadmonfared

doi:10.1109/ICCP.2012.6356195

Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems

Sepehr Damavandinejadmonfared^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

8 Citations (Scopus)

Abstract

In this paper, a new method for face recognition in video surveillance is proposed. Local mean-based k-nearest centroid neighbour (LMKNCN) is a recently proposed method for classifying data which has been proven to be more appropriate than other classifiers such as k-nearest neighbour (KNN), K-Nearest Centroid Neighbour (KNCN), and local mean-based k-nearest neighbour (LMKNN). Kernel Entropy Component Analysis is a new extension of 1-D PCA-based feature extractions methods enhancing the performance of PCA-based methods. In the proposed method in this paper, LMKNCN is used as a classifier in KPCA method. Moreover, the Extensive experiments on surveillance camera faces database (SCfaces) and Head Pose Image database reveal the significance of the proposed method.

Original language	English
Title of host publication	Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
Pages	253-256
Number of pages	4
DOIs	https://doi.org/10.1109/ICCP.2012.6356195
Publication status	Published - 2012
Externally published	Yes
Event	2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012 - Cluj-Napoca Duration: 30 Aug 2012 → 1 Sept 2012

Other

Other	2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
City	Cluj-Napoca
Period	30/08/12 → 1/09/12

Keywords

Biometrics
Face recognition
Kernel Entropy Component Analysis (KECA)
local mean-based k-nearest neighbor (LMKNN)
Principal component Analysis

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10.1109/ICCP.2012.6356195

Cite this

Damavandinejadmonfared, S. (2012). Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems. In Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012 (pp. 253-256). Article 6356195 https://doi.org/10.1109/ICCP.2012.6356195

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title = "Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems",

abstract = "In this paper, a new method for face recognition in video surveillance is proposed. Local mean-based k-nearest centroid neighbour (LMKNCN) is a recently proposed method for classifying data which has been proven to be more appropriate than other classifiers such as k-nearest neighbour (KNN), K-Nearest Centroid Neighbour (KNCN), and local mean-based k-nearest neighbour (LMKNN). Kernel Entropy Component Analysis is a new extension of 1-D PCA-based feature extractions methods enhancing the performance of PCA-based methods. In the proposed method in this paper, LMKNCN is used as a classifier in KPCA method. Moreover, the Extensive experiments on surveillance camera faces database (SCfaces) and Head Pose Image database reveal the significance of the proposed method.",

keywords = "Biometrics, Face recognition, Kernel Entropy Component Analysis (KECA), local mean-based k-nearest neighbor (LMKNN), Principal component Analysis",

author = "Sepehr Damavandinejadmonfared",

year = "2012",

doi = "10.1109/ICCP.2012.6356195",

language = "English",

isbn = "9781467329514",

pages = "253--256",

booktitle = "Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012",

note = "2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012 ; Conference date: 30-08-2012 Through 01-09-2012",

}

Damavandinejadmonfared, S 2012, Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems. in Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012., 6356195, pp. 253-256, 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012, Cluj-Napoca, 30/08/12. https://doi.org/10.1109/ICCP.2012.6356195

Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems. / Damavandinejadmonfared, Sepehr.
Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012. 2012. p. 253-256 6356195.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems

AU - Damavandinejadmonfared, Sepehr

PY - 2012

Y1 - 2012

N2 - In this paper, a new method for face recognition in video surveillance is proposed. Local mean-based k-nearest centroid neighbour (LMKNCN) is a recently proposed method for classifying data which has been proven to be more appropriate than other classifiers such as k-nearest neighbour (KNN), K-Nearest Centroid Neighbour (KNCN), and local mean-based k-nearest neighbour (LMKNN). Kernel Entropy Component Analysis is a new extension of 1-D PCA-based feature extractions methods enhancing the performance of PCA-based methods. In the proposed method in this paper, LMKNCN is used as a classifier in KPCA method. Moreover, the Extensive experiments on surveillance camera faces database (SCfaces) and Head Pose Image database reveal the significance of the proposed method.

AB - In this paper, a new method for face recognition in video surveillance is proposed. Local mean-based k-nearest centroid neighbour (LMKNCN) is a recently proposed method for classifying data which has been proven to be more appropriate than other classifiers such as k-nearest neighbour (KNN), K-Nearest Centroid Neighbour (KNCN), and local mean-based k-nearest neighbour (LMKNN). Kernel Entropy Component Analysis is a new extension of 1-D PCA-based feature extractions methods enhancing the performance of PCA-based methods. In the proposed method in this paper, LMKNCN is used as a classifier in KPCA method. Moreover, the Extensive experiments on surveillance camera faces database (SCfaces) and Head Pose Image database reveal the significance of the proposed method.

KW - Biometrics

KW - Face recognition

KW - Kernel Entropy Component Analysis (KECA)

KW - local mean-based k-nearest neighbor (LMKNN)

KW - Principal component Analysis

UR - https://www.scopus.com/pages/publications/84871564333

U2 - 10.1109/ICCP.2012.6356195

DO - 10.1109/ICCP.2012.6356195

M3 - Conference proceeding contribution

AN - SCOPUS:84871564333

SN - 9781467329514

SP - 253

EP - 256

BT - Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012

T2 - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012

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ER -

Damavandinejadmonfared S. Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems. In Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012. 2012. p. 253-256. 6356195 doi: 10.1109/ICCP.2012.6356195

Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems

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