Matching Larger Image Areas for Unconstrained Face Identification

Jack Gaston, Ming Ji, Danny Crookes

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
165 Downloads (Pure)


Many approaches to unconstrained face identification exploit small patches which are unaffected by distortions outside of their locality. A larger area usually contains more discriminative information, but may be unidentifiable due to local appearance changes across its area, given limited training data. We propose a novel block-based approach, as a complement to existing patch-based approaches, to exploit the greater discriminative information in larger areas,
while maintaining robustness to limited training data. A testing block contains several neighboring patches, each of a small size. We identify the matching training block by jointly estimating all the matching patches, as a means of reducing the uncertainty of each small matching patch with the addition of the neighboring patch information, without assuming additional training data. We
further propose a multi-scale extension in which we carry out block-based matching at several block sizes, to combine complementary information across scales for further robustness. We have conducted face identification experiments using three datasets, the constrained GT dataset to validate the new approach, and two unconstrained datasets, LFW and UFI, to evaluate its potential for improving robustness. The results show that the new approach is able to significantly improve over existing patch-based face identification approaches, in the presence of uncontrolled pose, expression and lighting variations, using small training datasets. It is also shown that the new block-based scheme can be combined with existing approaches to further improve performance.
Original languageEnglish
Pages (from-to)3191-3202
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number8
Early online date28 Jun 2018
Publication statusEarly online date - 28 Jun 2018

Bibliographical note

Under second run of review with minor changes


  • Block-based correlation, face identification, multipatch blocks, multiscale combination, patch-based image processing


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