Face and texture analysis using local descriptors: A comparative analysis

A. Hadid; J. Ylioinas; Miguel Bordallo Lopez

doi:10.1109/IPTA.2014.7001944

Face and texture analysis using local descriptors: A comparative analysis

A. Hadid, J. Ylioinas, Miguel Bordallo Lopez

Not published at VTT

University of Oulu

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

10 Citations (Scopus)

Abstract

In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proved to be more effective in real world conditions. This paper considers three recent yet popular local descriptors, namely Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF), and provides extensive comparative analysis on two different research problems (gender and texture classification) using benchmark datasets. The three descriptors are analyzed in terms of both classification accuracy and computational costs. Furthermore, experiments on combining these descriptors are provided, pointing out useful insight into their complementarity.

Original language	English
Title of host publication	4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014
Publisher	IEEE Institute of Electrical and Electronic Engineers
Number of pages	4
ISBN (Electronic)	978-1-4799-6463-5
ISBN (Print)	978-1-4799-6462-8
DOIs	https://doi.org/10.1109/IPTA.2014.7001944
Publication status	Published - 2014
MoE publication type	A4 Article in a conference publication
Event	IEEE International conference in image processing tools and applications - France, Orleans, France Duration: 15 Oct 2014 → …

Conference

Conference	IEEE International conference in image processing tools and applications
Country/Territory	France
City	Orleans
Period	15/10/14 → …

Keywords

Local descriptors
texture analysis
gender classification

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10.1109/IPTA.2014.7001944

Cite this

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title = "Face and texture analysis using local descriptors: A comparative analysis",

abstract = "In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proved to be more effective in real world conditions. This paper considers three recent yet popular local descriptors, namely Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF), and provides extensive comparative analysis on two different research problems (gender and texture classification) using benchmark datasets. The three descriptors are analyzed in terms of both classification accuracy and computational costs. Furthermore, experiments on combining these descriptors are provided, pointing out useful insight into their complementarity.",

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Hadid, A, Ylioinas, J & Bordallo Lopez, M 2014, Face and texture analysis using local descriptors: A comparative analysis. in 4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014. IEEE Institute of Electrical and Electronic Engineers, IEEE International conference in image processing tools and applications , Orleans, France, 15/10/14. https://doi.org/10.1109/IPTA.2014.7001944

Face and texture analysis using local descriptors: A comparative analysis. / Hadid, A.; Ylioinas, J.; Bordallo Lopez, Miguel.
4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014. IEEE Institute of Electrical and Electronic Engineers, 2014.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Face and texture analysis using local descriptors

T2 - IEEE International conference in image processing tools and applications

AU - Hadid, A.

AU - Ylioinas, J.

AU - Bordallo Lopez, Miguel

PY - 2014

Y1 - 2014

N2 - In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proved to be more effective in real world conditions. This paper considers three recent yet popular local descriptors, namely Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF), and provides extensive comparative analysis on two different research problems (gender and texture classification) using benchmark datasets. The three descriptors are analyzed in terms of both classification accuracy and computational costs. Furthermore, experiments on combining these descriptors are provided, pointing out useful insight into their complementarity.

AB - In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proved to be more effective in real world conditions. This paper considers three recent yet popular local descriptors, namely Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF), and provides extensive comparative analysis on two different research problems (gender and texture classification) using benchmark datasets. The three descriptors are analyzed in terms of both classification accuracy and computational costs. Furthermore, experiments on combining these descriptors are provided, pointing out useful insight into their complementarity.

KW - Local descriptors

KW - texture analysis

KW - gender classification

U2 - 10.1109/IPTA.2014.7001944

DO - 10.1109/IPTA.2014.7001944

M3 - Conference article in proceedings

SN - 978-1-4799-6462-8

BT - 4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014

PB - IEEE Institute of Electrical and Electronic Engineers

Y2 - 15 October 2014

ER -

Face and texture analysis using local descriptors: A comparative analysis

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