Regularized metric adaptation for unconstrained face verification

Boyu Lu, Jun Cheng Chen, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this work, we propose a metric adaptation method for set-based face verification and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version, the Janus Challenging Set 2 (CS2). A template-specific metric is trained to adaptively learn the discriminative information in test templates and the negative training set, which contains subjects that are mutually exclusive to subjects in test templates. The proposed regularized joint Bayesian metric learning framework not only alleviates the over-fitting problem but also provides a way to efficiently reduce the model size. We also analyze the selection of the compact and representative negative set to speed up the training time and to reduce storage space. Experiments on the IJB-A and CS2 datasets yield promising results.

Original languageEnglish (US)
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509048472
StatePublished - Jan 1 2016
Externally publishedYes
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: Dec 4 2016Dec 8 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other23rd International Conference on Pattern Recognition, ICPR 2016

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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