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One-Shot Face Recognition Based on Multiple Classifiers Training

Published: 21 June 2021 Publication History

Abstract

One-shot face recognition is a challenging problem which requires recognizing novel identities from only one seen face image. One-shot classes are simply neglected because of the lack of training samples. Therefore, these classes contribute less to the improvement of face recognition performance. The main goal of one-shot face recognition task is to use the novel face samples to enhance the ability of network not only in close-set classify, but also in open-set face verification. In this paper, Base data and Novel data is trained separately with two classifiers to reduce the impact of data imbalance. We propose Confidence Constrain Loss to train classifiers in parallel and get better classifiers fusion in the test phase. Besides, we use data augmentation with 3D face reconstruction to obtain a variety of oneshot set's training samples. Thus, our method can effectively increase the recognition accuracy in the novel set without reducing recognition accuracy in base set. Experiments on MS-celeb-1M low-shot dataset demonstrate that our method achieve state-of-the-art which has 98.90% coverage at precision=99% without using external data.

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  • (2023)Structural plasticity‐based hydrogel optical Willshaw model for one‐shot on‐the‐fly edge learningInfoMat10.1002/inf2.123995:4Online publication date: 20-Jan-2023

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ICMLC '21: Proceedings of the 2021 13th International Conference on Machine Learning and Computing
February 2021
601 pages
ISBN:9781450389310
DOI:10.1145/3457682
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 21 June 2021

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  1. Confidence constrain
  2. Data augmentation
  3. Multiple classifiers
  4. Oneshot face recognition

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  • (2023)Structural plasticity‐based hydrogel optical Willshaw model for one‐shot on‐the‐fly edge learningInfoMat10.1002/inf2.123995:4Online publication date: 20-Jan-2023

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