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Discriminative pose-free descriptors for face and object matching

Sanyal, Soubhik and Mudunuri, Sivaram Prasad and Biswas, Soma (2017) Discriminative pose-free descriptors for face and object matching. In: PATTERN RECOGNITION, 67 . pp. 353-365.

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Official URL: https://doi.org/10.1016/j.patcog.2017.02.016

Abstract

Pose invariant matching is a very important problem with various applications like recognizing faces in uncontrolled scenarios in which the facial images appear in wide variety of pose and illumination conditions along with low resolution. Here we propose two discriminative pose-free descriptors, Subspace Point Representation (DPF-SPR) and Layered Canonical Correlated (DPF-LCC) descriptor, for matching faces and objects across pose. Training examples at very few poses are used to generate virtual intermediate pose subspaces. An image is represented by a feature set obtained by projecting its low-level feature on these subspaces and a discriminative transform is applied to make this feature set suitable for recognition. We represent this discriminative feature set by two novel descriptors. In one approach, we transform it to a vector by using subspace to point representation technique. In the second approach, a layered structure of canonical correlated subspaces are formed, onto which the feature set is projected. Experiments on recognizing faces and objects across pose and comparisons with state-of-the-art show the effectiveness of the proposed approach. (C) 2017 Elsevier Ltd. All rights reserved.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 20 May 2017 05:53
Last Modified: 20 May 2017 05:53
URI: http://eprints.iisc.ac.in/id/eprint/56930

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