Derivative Variation Pattern for Illumination-Invariant Image Representation

M. Tavakolian; F. Hajati; Ajmal Mian; Y. Gao; S. Gheisari

doi:10.1109/ICIP.2013.6738867

Derivative Variation Pattern for Illumination-Invariant Image Representation

M. Tavakolian, F. Hajati, Ajmal Mian, Y. Gao, S. Gheisari

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

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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	Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013
Place of Publication	Australia
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	4210-4214
ISBN (Print)	9781479923410
DOIs	https://doi.org/10.1109/ICIP.2013.6738867
Publication status	Published - Sept 2013
Event	2013 IEEE International Conference on Image Processing - Melbourne, Australia, Melbourne, Australia Duration: 15 Sept 2013 → 18 Sept 2013 Conference number: 20th

Conference

Conference	2013 IEEE International Conference on Image Processing
Abbreviated title	ICIP 2013
Country/Territory	Australia
City	Melbourne
Period	15/09/13 → 18/09/13

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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 = "M. Tavakolian and F. Hajati and Ajmal Mian and Y. Gao and S. Gheisari",

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Tavakolian, M, Hajati, F, Mian, A, Gao, Y & Gheisari, S 2013, Derivative Variation Pattern for Illumination-Invariant Image Representation. in Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013. IEEE, Institute of Electrical and Electronics Engineers, Australia, pp. 4210-4214, 2013 IEEE International Conference on Image Processing, Melbourne, Victoria, Australia, 15/09/13. https://doi.org/10.1109/ICIP.2013.6738867

Derivative Variation Pattern for Illumination-Invariant Image Representation. / Tavakolian, M.; Hajati, F.; Mian, Ajmal et al.
Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013. Australia: IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 4210-4214.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

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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.

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Derivative Variation Pattern for Illumination-Invariant Image Representation

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