short-paper

Robust Deep Auto-encoder for Occluded Face Recognition

Authors:

Lele Cheng,

Jinjun Wang,

Yihong Gong,

Qiqi HouAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 1099 - 1102

https://doi.org/10.1145/2733373.2806291

Published: 13 October 2015 Publication History

Get Access

Abstract

Occlusions by sunglasses, scarf, hats, beard, shadow etc, can significantly reduce the performance of face recognition systems. Although there exists a rich literature of researches focusing on face recognition with illuminations, poses and facial expression variations, there is very limited work reported for occlusion robust face recognition. In this paper, we present a method to restore occluded facial regions using deep learning technique to improve face recognition performance. Inspired by SSDA for facial occlusion removal with known occlusion type and explicit occlusion location detection from a preprocessing step, this paper further introduces Double Channel SSDA (DC-SSDA) which requires no prior knowledge of the types and the locations of occlusions. Experimental results based on CMU-PIE face database have showed that, the proposed method is robust to a variety of occlusion types and locations, and the restored faces could yield significant recognition performance improvements over occluded ones.

References

[1]

Bengio and Yoshua. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2009.

Digital Library

Google Scholar

[2]

H. C. Burger, C. J. Schuler, and S. Harmeling. Image denoising: Can plain neural networks compete with bm3d? In CVPR, 2012.

Crossref

Google Scholar

[3]

V. Jain and H. Seung. Natural image denoising with convolutional networks. In NIPS, 2008.

Digital Library

Google Scholar

[4]

H. Li, G. Hua, Z. Lin, J. Brandt, and J. Yang. Probabilistic elastic matching for pose variant face verification. In CVPR, 2013.

Digital Library

Google Scholar

[5]

M. Ranzato, J. Susskind, V. Mnih, and G. Hinton. On deep generative models with applications to recognition. In CVPR, 2011.

Digital Library

Google Scholar

[6]

T. Sim, S. Baker, and M. Bsat. The cmu pose and illumination and and expression database. PAMI, 2003.

Digital Library

Google Scholar

[7]

Y. Tang, R. Salakhutdinov, and G. Hinton. Robust boltzmann machines for recognition and denoising. In CVPR, 2012.

Digital Library

Google Scholar

[8]

P. Vincent, H. Larochelle, and I. Lajoie. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010.

Digital Library

Google Scholar

[9]

J. Wright, Y. Peng, and Y. Ma. Robust principal component analysis:exact recovery of corrupted low-rank matrices by convex optimization. In NIPS, 2009.

Google Scholar

[10]

J. Xie, L. Xu, and E. Chen. Image denoising and inpainting with deep neural networks. In NIPS, 2012.

Digital Library

Google Scholar

[11]

X. Xie and K. Lam. Face recognition under varying illumination based on a 2d face shape model. Pattern Recognition, 2005.

Google Scholar

Cited By

View all

Sang GZeng DYan CVeldhuis RSpreeuwers L(2024)Robust partial face recognition using multi-label attributesIntelligent Data Analysis10.3233/IDA-22730928:1(377-392)Online publication date: 3-Feb-2024
https://doi.org/10.3233/IDA-227309
Malakar SChiracharit WChamnongthai K(2024)Masked Face Recognition With Generated Occluded Part Using Image Augmentation and CNN Maintaining Face IdentityIEEE Access10.1109/ACCESS.2024.344665212(126356-126375)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3446652
Darshan LNagasundara K(2024)A survey on disguise face recognitionJournal of the Chinese Institute of Engineers10.1080/02533839.2024.234649447:5(528-543)Online publication date: 6-May-2024
https://doi.org/10.1080/02533839.2024.2346494
Show More Cited By

Index Terms

Robust Deep Auto-encoder for Occluded Face Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
  2. Machine learning

Recommendations

Robust face recognition based on dynamic rank representation

Robust face recognition is an active topic in computer vision, while face occlusion is one of the most challenging problems for robust face recognition algorithm. The latest research on low-rank representation demonstrated its high efficiency to ...
Age-Invariant Face Recognition

One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that ...
Illumination-robust face recognition based on Gabor feature face intrinsic identity PCA model
CIMMACS'08: Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics

Robust face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. However, there is no feature vector invariant under illumination ...

Comments

Information & Contributors

Information

Published In

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

National Science Foundation of China
National High Technology Research and Development Program of China

Conference

MM '15

Sponsor:

SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

Acceptance Rates

MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

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

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

28
Total Citations
View Citations
730
Total Downloads

Downloads (Last 12 months)23
Downloads (Last 6 weeks)4

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sang GZeng DYan CVeldhuis RSpreeuwers L(2024)Robust partial face recognition using multi-label attributesIntelligent Data Analysis10.3233/IDA-22730928:1(377-392)Online publication date: 3-Feb-2024
https://doi.org/10.3233/IDA-227309
Malakar SChiracharit WChamnongthai K(2024)Masked Face Recognition With Generated Occluded Part Using Image Augmentation and CNN Maintaining Face IdentityIEEE Access10.1109/ACCESS.2024.344665212(126356-126375)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3446652
Darshan LNagasundara K(2024)A survey on disguise face recognitionJournal of the Chinese Institute of Engineers10.1080/02533839.2024.234649447:5(528-543)Online publication date: 6-May-2024
https://doi.org/10.1080/02533839.2024.2346494
Yang XHan MLuo YHu HWen Y(2023)Two-Stream Prototype Learning Network for Few-Shot Face Recognition Under OcclusionsIEEE Transactions on Multimedia10.1109/TMM.2023.325305425(1555-1563)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2023.3253054
Zhang NLiu NHan JWan KShao L(2023)Face De-Occlusion With Deep Cascade Guidance LearningIEEE Transactions on Multimedia10.1109/TMM.2022.315703625(3217-3229)Online publication date: 2023
https://doi.org/10.1109/TMM.2022.3157036
Anusha SNimala K(2023)Analysis of Various Learning Approaches in Occluded Face Recognition2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)10.1109/ICAEECI58247.2023.10370860(1-8)Online publication date: 19-Oct-2023
https://doi.org/10.1109/ICAEECI58247.2023.10370860
Swearingen TRoss A(2022)Synthesizing Face Images from Match Scores2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)10.1109/WACVW54805.2022.00035(292-300)Online publication date: Jan-2022
https://doi.org/10.1109/WACVW54805.2022.00035
He MZhang JShan SLiu XWu ZChen X(2022)Locality-Aware Channel-Wise Dropout for Occluded Face RecognitionIEEE Transactions on Image Processing10.1109/TIP.2021.313282731(788-798)Online publication date: 2022
https://doi.org/10.1109/TIP.2021.3132827
Hao SChen CChen ZWong K(2022)A Unified Framework for Masked and Mask-Free Face Recognition Via Feature Rectification2022 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP46576.2022.9897292(726-730)Online publication date: 16-Oct-2022
https://doi.org/10.1109/ICIP46576.2022.9897292
Jiang WYe LYi ZPeng C(2022)A new occluded face recognition framework with combination of both Deocclusion and feature filtering methodsMultimedia Tools and Applications10.1007/s11042-022-12851-x81:23(33867-33896)Online publication date: 21-Apr-2022
https://doi.org/10.1007/s11042-022-12851-x
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Robust face recognition based on dynamic rank representation

Age-Invariant Face Recognition

Illumination-robust face recognition based on Gabor feature face intrinsic identity PCA model

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations