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Cross-Modal face matching: Beyond viewed sketches

Ouyang, S., Hospedales, T., Song, Yi-Zhe and Li, X. (2015) Cross-Modal face matching: Beyond viewed sketches In: 12th Asian Conference on Computer Vision, 01-05 Nov 2014, Singapore, Singapore.

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Abstract

Matching face images across different modalities is a challenging open problem for various reasons, notably feature heterogeneity, and particularly in the case of sketch recognition – abstraction, exaggeration and distortion. Existing studies have attempted to address this task by engineering invariant features, or learning a common subspace between the modalities. In this paper, we take a different approach and explore learning a mid-level representation within each domain that allows faces in each modality to be compared in a domain invariant way. In particular, we investigate sketch-photo face matching and go beyond the well-studied viewed sketches to tackle forensic sketches and caricatures where representations are often symbolic. We approach this by learning a facial attribute model independently in each domain that represents faces in terms of semantic properties. This representation is thus more invariant to heterogeneity, distortions and robust to mis-alignment. Our intermediate level attribute representation is then integrated synergistically with the original low-level features using CCA. Our framework shows impressive results on cross-modal matching tasks using forensic sketches, and even more challenging caricature sketches. Furthermore, we create a new dataset with ≈ 59, 000 attribute annotations for evaluation and to facilitate future research.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Ouyang, S.
Hospedales, T.
Song, Yi-Zhey.song@surrey.ac.uk
Li, X.
Date : 2015
DOI : 10.1007/978-3-319-16808-1_15
Depositing User : Clive Harris
Date Deposited : 12 Aug 2019 09:41
Last Modified : 12 Aug 2019 09:41
URI: http://epubs.surrey.ac.uk/id/eprint/852133

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