Confidence-Driven Network for Point-to-Set Matching

Mengjun Leng, Ioannis A. Kakadiaris

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


The goal of point-to-set matching is to match a single image with a set of images from a subject. Within an image set, different images contain various levels of discriminative information and thus should contribute differently to the results. However, the discriminative level is not accessible directly. To this end, we propose a confidence driven network to perform point-to-set matching. The proposed system comprises a feature extraction network (FEN) and a performance prediction network (PPN). Given an input image, the FEN generates a template, while the PPN generates a confidence score which measures the discriminative level of the template. At matching time, the template is used to compute a point-to-point similarity. The similarity scores from different samples in the set are integrated at a score level, weighted by the predicted confidence scores. Extensive multi-probe face recognition experiments on the IJB-A and UHDB-31 datasets demonstrate performance improvements over state of the art algorithms.

Original languageEnglish (US)
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781538637883
StatePublished - Nov 26 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: Aug 20 2018Aug 24 2018

Publication series

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


Conference24th International Conference on Pattern Recognition, ICPR 2018

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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