Improved low resolution heterogeneous face recognition using re-ranking

Mudunuri, SP and Venkataramanan, S and Biswas, S (2018) Improved low resolution heterogeneous face recognition using re-ranking. In: 6th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2017, 16 - 19 December 2017, Mandi, pp. 446-456.

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Abstract

Recently, near-infrared to visible light facial image matching is gaining popularity, especially for low-light and night-time surveillance scenarios. Unlike most of the work in literature, we assume that the near-infrared probe images have low-resolution in addition to uncontrolled pose and expression, which is due to the large distance of the person from the camera. To address this very challenging problem, we propose a re-ranking strategy which takes into account the relation of both the probe and gallery with a set of reference images. This can be used as an add-on to any existing algorithm. We apply it with one recent dictionary learning algorithm which uses alignment of orthogonal dictionaries. We also create a benchmark for this task by evaluating some of the recent algorithms for this experimental protocol. Extensive experiments are conducted on a modified version of the CASIA NIR VIS 2.0 database to show the effectiveness of the proposed re-ranking approach.

Item Type:	Conference Paper
Publication:	Communications in Computer and Information Science
Publisher:	Springer Verlag
Additional Information:	The copyright for this article belongs to the Springer Nature Singapore Pte Ltd.
Keywords:	Computer vision; Infrared devices; Learning algorithms; Light; Probes, Dictionary learning algorithms; Experimental protocols; Heterogeneous face recognition; Low resolution; Near Infrared; Near-infrared to visible; Re-ranking; Reference image, Face recognition
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	26 Aug 2022 06:26
Last Modified:	26 Aug 2022 06:26
URI:	https://eprints.iisc.ac.in/id/eprint/76075

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