research-article

Face Recognition via Active Annotation and Learning

Authors:

Xiangyang XueAuthors Info & Claims

MM '16: Proceedings of the 24th ACM international conference on Multimedia

Pages 1058 - 1062

https://doi.org/10.1145/2964284.2984059

Published: 01 October 2016 Publication History

Abstract

In this paper, we introduce an active annotation and learning framework for the face recognition task. Starting with an initial label deficient face image training set, we iteratively train a deep neural network and use this model to choose the examples for further manual annotation. We follow the active learning strategy and derive the Value of Information criterion to actively select candidate annotation images. During these iterations, the deep neural network is incrementally updated. Experimental results conducted on LFW benchmark and MS-Celeb-1M challenge demonstrate the effectiveness of our proposed framework.

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Cited By

Alam GMcChesney INicholl PRafferty J(2023)Open Datasets in Human Activity Recognition Research—Issues and Challenges: A ReviewIEEE Sensors Journal10.1109/JSEN.2023.331764523:22(26952-26980)Online publication date: 15-Nov-2023
https://doi.org/10.1109/JSEN.2023.3317645
Zhang YWang YZhang HZhu BChen SZhang D(2022)OneLabeler: A Flexible System for Building Data Labeling ToolsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517612(1-22)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517612
Wu XXiao LSun YZhang JMa THe L(2022)A survey of human-in-the-loop for machine learningFuture Generation Computer Systems10.1016/j.future.2022.05.014135(364-381)Online publication date: Oct-2022
https://doi.org/10.1016/j.future.2022.05.014
Show More Cited By

Index Terms

Face Recognition via Active Annotation and Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics

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Published In

cover image ACM Conferences

MM '16: Proceedings of the 24th ACM international conference on Multimedia

October 2016

1542 pages

ISBN:9781450336031

DOI:10.1145/2964284

General Chairs:
Alan Hanjalic
Delft University of Technology
,
Cees Snoek
Qualcomm Research Netherlands / University of Amsterdam
,
Marcel Worring
University of Amsterdam
,
Moderator:
Dick Bulterman
CWI / VU University Amsterdam
,
Program Chairs:
Benoit Huet
EURECOM
,
Aisling Kelliher
Virginia Tech
,
Yiannis Kompatsiaris
CERTH-ITI
,
Jin Li
Microsoft

Copyright © 2016 ACM.

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: 01 October 2016

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MM '16: ACM Multimedia Conference

October 15 - 19, 2016

Amsterdam, The Netherlands

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MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Alam GMcChesney INicholl PRafferty J(2023)Open Datasets in Human Activity Recognition Research—Issues and Challenges: A ReviewIEEE Sensors Journal10.1109/JSEN.2023.331764523:22(26952-26980)Online publication date: 15-Nov-2023
https://doi.org/10.1109/JSEN.2023.3317645
Zhang YWang YZhang HZhu BChen SZhang D(2022)OneLabeler: A Flexible System for Building Data Labeling ToolsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517612(1-22)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517612
Wu XXiao LSun YZhang JMa THe L(2022)A survey of human-in-the-loop for machine learningFuture Generation Computer Systems10.1016/j.future.2022.05.014135(364-381)Online publication date: Oct-2022
https://doi.org/10.1016/j.future.2022.05.014
Nieddu LVicari D(2020)Supervised Nested Algorithm for Classification Based on K-MeansAdvanced Studies in Classification and Data Science10.1007/978-981-15-3311-2_7(79-88)Online publication date: 26-Sep-2020
https://doi.org/10.1007/978-981-15-3311-2_7
Pala GEroglu Erdem C(2019)Performance Comparison of Deep Learning Based Face Identification Methods for Video Under Adverse Conditions2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2019.00026(90-97)Online publication date: Nov-2019
https://doi.org/10.1109/SITIS.2019.00026
Jin CJin RChen KDou Y(2018)A Community Detection Approach to Cleaning Extremely Large Face DatabaseComputational Intelligence and Neuroscience10.1155/2018/45124732018Online publication date: 22-Apr-2018
https://dl.acm.org/doi/10.1155/2018/4512473
Zeng JLiu SLi XMahdi DWu FWang G(2018)Deep Context-Sensitive Facial Landmark Detection With Tree-Structured ModelingIEEE Transactions on Image Processing10.1109/TIP.2017.278457127:5(2096-2107)Online publication date: May-2018
https://doi.org/10.1109/TIP.2017.2784571
Sun JHe HLuo HZhang L(2018)Smoothness Assisted Interactive Face Annotation via Neural NetworkAdvances in Multimedia Information Processing – PCM 201810.1007/978-3-030-00776-8_22(235-245)Online publication date: 19-Sep-2018
https://doi.org/10.1007/978-3-030-00776-8_22
Wu YLiu HLi JFu YWu WYang JTian QZimmermann R(2017)Deep Face Recognition with Center Invariant LossProceedings of the on Thematic Workshops of ACM Multimedia 201710.1145/3126686.3126693(408-414)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3126686.3126693
Wang ZHe KFu YFeng RJiang YXue XIonescu BSebe NFeng JLarson MLienhart RSnoek C(2017)Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute PredictionProceedings of the 2017 ACM on International Conference on Multimedia Retrieval10.1145/3078971.3078973(365-374)Online publication date: 6-Jun-2017
https://dl.acm.org/doi/10.1145/3078971.3078973
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