Unconstrained Face Recognition Using A Set-to-Set Distance Measure on Deep Learned Features

Zhao, Jiaojiao and Han, Jungong and Shao, Ling (2018) Unconstrained Face Recognition Using A Set-to-Set Distance Measure on Deep Learned Features. IEEE Transactions on Circuits and Systems for Video Technology, 28 (10). pp. 2679-2689. ISSN 1051-8215

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Abstract

Recently considerable efforts have been dedicated to unconstrained face recognition, which requires to identify faces "in the wild" for a set of images and/or video frames captured without human intervention. Unlike traditional face recognition that compares one-to-one medium (either a single image or a video frame) only, we consider a problem of matching sets with heterogeneous contents of both images and videos. In this paper, we propose a novel Set-to-Set (S2S) distance measure to calculate the similarity between two sets with the aim to improve the accuracy of face recognition in real-world situations such as extreme poses or severe illumination conditions. Our S2S distance adopts the kNN-average pooling for the similarity scores computed on all the media in two sets, making the identification far less susceptible to the poor representations (outliers) than traditional feature-average pooling and score-average pooling. Furthermore, we show that various metrics can be embedded into our S2S distance framework, including both predefined and learned ones. This allows to choose the appropriate metric depending on the recognition task in order to achieve the best results. To evaluate the proposed S2S distance, we conduct extensive experiments on the challenging set-based IJB-A face dataset, which demonstrate that our algorithm achieves the stateof- the-art results and is clearly superior to the baselines including several deep learning based face recognition algorithms.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Circuits and Systems for Video Technology
Additional Information:
©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
87886
Deposited By:
Deposited On:
20 Sep 2017 15:12
Refereed?:
Yes
Published?:
Published
Last Modified:
18 Sep 2020 03:43