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Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Open-Set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
Organization Unit
Authors
  • Rafael Henrique Vareto
  • Manuel Günther
  • William Robson Schwartz
Presentation Type paper
Item Subtype Original Work
Refereed No
Status Published in final form
Language
  • English
ISBN 979-8-3503-3872-0
ISSN 2377-5416
Page Range 55 - 60
Event Title 2023 36th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
Event Type conference
Event Location Rio Grande, Brazil
Event Start Date November 6 - 2023
Event End Date November 9 - 2023
Series Name Proceedings Conference on Graphics, Patterns and Images (SIBGRAPI)
Publisher Institute of Electrical and Electronics Engineers
Abstract Text Open-set face recognition is a scenario in which biometric systems have incomplete knowledge of all existing subjects. This arduous requirement must dismiss irrelevant faces and focus on subjects of interest only. For this reason, this work introduces a novel method that associates an ensemble of compact neural networks with data augmentation at the feature level and an entropy-based cost function. Deep neural networks pre-trained on large face datasets serve as the preliminary feature extraction module. The neural adapter ensemble consists of binary models trained on original feature representations along with negative synthetic mix-up embeddings, which are adequately handled by the designed open-set loss since they do not belong to any known identity. We carry out experiments on well-known LFW and IJB-C datasets where results show that the approach is capable of boosting closed and open-set identification accuracy.
Digital Object Identifier 10.1109/sibgrapi59091.2023.10347168
Other Identification Number merlin-id:24396
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