Derivative variation pattern for illumination-invariant image representation

Mohammad Tavakolian; Farshid Hajati; Ajmal S Mian; Yongsheng Gao; Soheila Gheisari

Derivative variation pattern for illumination-invariant image representation

Mohammad Tavakolian
, Farshid Hajati
, Ajmal S Mian
, Yongsheng Gao
, Soheila Gheisari

Computer Science

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

Abstract

This paper presents a novel image descriptor called Derivative Variation Pattern (DVP) and its application to face and palmprint recognition. DVP captures image variations in both the frequency and the spatial domains. The effects of uncontrolled illumination are compensated in the frequency domain by discarding the illumination affected frequencies. Image pixels are encoded as binary patterns based on the higher-order spatial derivatives computed in the spatial domain. The proposed descriptor was evaluated on the Extended Yale-B and FERET face databases, and the PolyU palmprint database. Experimental results demonstrate the effectiveness of the DVP descriptor in both the face and the palmprint recognition tasks under uncontrolled illuminations.

Original language	English
Title of host publication	2013 IEEE International Conference on Image Processing15-18 September, 2013
Place of Publication	United States of America
Publisher	Institute of Electrical and Electronics Engineers
Pages	4210-4214
ISBN (Print)	9781479923410
Publication status	Published - 13 Feb 2014
Event	2013 IEEE International Conference on Image Processing - Melbourne Convention and Exhibition Centre (MCEC), Melbourne, Australia Duration: 15 Sept 2013 → 18 Sept 2013

Conference

Conference	2013 IEEE International Conference on Image Processing
City	Melbourne, Australia
Period	15/09/13 → 18/09/13

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@inproceedings{63db85acfe8c41348cbc91a3e88419ae,

title = "Derivative variation pattern for illumination-invariant image representation",

abstract = "This paper presents a novel image descriptor called Derivative Variation Pattern (DVP) and its application to face and palmprint recognition. DVP captures image variations in both the frequency and the spatial domains. The effects of uncontrolled illumination are compensated in the frequency domain by discarding the illumination affected frequencies. Image pixels are encoded as binary patterns based on the higher-order spatial derivatives computed in the spatial domain. The proposed descriptor was evaluated on the Extended Yale-B and FERET face databases, and the PolyU palmprint database. Experimental results demonstrate the effectiveness of the DVP descriptor in both the face and the palmprint recognition tasks under uncontrolled illuminations.",

author = "Mohammad Tavakolian and Farshid Hajati and Mian, \{Ajmal S\} and Yongsheng Gao and Soheila Gheisari",

year = "2014",

month = feb,

day = "13",

language = "English",

isbn = "9781479923410",

pages = "4210--4214",

booktitle = "2013 IEEE International Conference on Image Processing15-18 September, 2013",

publisher = "Institute of Electrical and Electronics Engineers",

note = "2013 IEEE International Conference on Image Processing ; Conference date: 15-09-2013 Through 18-09-2013",

}

Tavakolian, M, Hajati, F, Mian, AS, Gao, Y & Gheisari, S 2014, Derivative variation pattern for illumination-invariant image representation. in 2013 IEEE International Conference on Image Processing15-18 September, 2013. Institute of Electrical and Electronics Engineers, United States of America, pp. 4210-4214, 2013 IEEE International Conference on Image Processing, Melbourne, Australia, 15/09/13. <https://hdl.handle.net/1959.11/70759>

Derivative variation pattern for illumination-invariant image representation. / Tavakolian, Mohammad; Hajati, Farshid; Mian, Ajmal S et al.
2013 IEEE International Conference on Image Processing15-18 September, 2013. United States of America: Institute of Electrical and Electronics Engineers, 2014. p. 4210-4214.

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

TY - GEN

T1 - Derivative variation pattern for illumination-invariant image representation

AU - Tavakolian, Mohammad

AU - Hajati, Farshid

AU - Mian, Ajmal S

AU - Gao, Yongsheng

AU - Gheisari, Soheila

PY - 2014/2/13

Y1 - 2014/2/13

N2 - This paper presents a novel image descriptor called Derivative Variation Pattern (DVP) and its application to face and palmprint recognition. DVP captures image variations in both the frequency and the spatial domains. The effects of uncontrolled illumination are compensated in the frequency domain by discarding the illumination affected frequencies. Image pixels are encoded as binary patterns based on the higher-order spatial derivatives computed in the spatial domain. The proposed descriptor was evaluated on the Extended Yale-B and FERET face databases, and the PolyU palmprint database. Experimental results demonstrate the effectiveness of the DVP descriptor in both the face and the palmprint recognition tasks under uncontrolled illuminations.

AB - This paper presents a novel image descriptor called Derivative Variation Pattern (DVP) and its application to face and palmprint recognition. DVP captures image variations in both the frequency and the spatial domains. The effects of uncontrolled illumination are compensated in the frequency domain by discarding the illumination affected frequencies. Image pixels are encoded as binary patterns based on the higher-order spatial derivatives computed in the spatial domain. The proposed descriptor was evaluated on the Extended Yale-B and FERET face databases, and the PolyU palmprint database. Experimental results demonstrate the effectiveness of the DVP descriptor in both the face and the palmprint recognition tasks under uncontrolled illuminations.

UR - https://ieeexplore.ieee.org/document/6738867/authors#authors

M3 - Conference contribution

SN - 9781479923410

SP - 4210

EP - 4214

BT - 2013 IEEE International Conference on Image Processing15-18 September, 2013

PB - Institute of Electrical and Electronics Engineers

CY - United States of America

T2 - 2013 IEEE International Conference on Image Processing

Y2 - 15 September 2013 through 18 September 2013

ER -

Derivative variation pattern for illumination-invariant image representation

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