short-paper

Accio: A Data Set for Face Track Retrieval in Movies Across Age

Authors:

Makarand Tapaswi,

Hazim Kemal Ekenel,

Rainer StiefelhagenAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 455 - 458

https://doi.org/10.1145/2671188.2749296

Published: 22 June 2015 Publication History

Abstract

Video face recognition is a very popular task and has come a long way. The primary challenges such as illumination, resolution and pose are well studied through multiple data sets. However there are no video-based data sets dedicated to study the effects of aging on facial appearance. We present a challenging face track data set, Harry Potter Movies Aging Data set (Accio¹), to study and develop age invariant face recognition methods for videos. Our data set not only has strong challenges of pose, illumination and distractors, but also spans a period of ten years providing substantial variation in facial appearance. We propose two primary tasks: within and across movie face track retrieval; and two protocols which differ in their freedom to use external data. We present baseline results for the retrieval performance using a state-of-the-art face track descriptor. Our experiments show clear trends of reduction in performance as the age gap between the query and database increases. We will make the data set publicly available for further exploration in age-invariant video face recognition.

References

[1]

FG-NET Aging Database.

[2]

M. Bäuml, M. Tapaswi, and R. Stiefelhagen. Semi-supervised Learning with Constraints for Person Identification in Multimedia Data. In CVPR, 2013.

Digital Library

[3]

B.-C. Chen, C.-S. Chen, and W. H. Hsu. Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval. In ECCV, 2014.

[4]

D. Chen, X. Cao, F. Wen, and J. Sun. Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification. In CVPR, 2013.

Digital Library

[5]

M. Everingham, J. Sivic, and A. Zisserman. "Hello! My name is... Buffy" - Automatic Naming of Characters in TV Video. In BMVC, 2006.

[6]

R. Feris, R. Bobbitt, L. Brown, and S. Pankati. Attribute-based People Search: Lessons Learnt from a Practical Surveillance System. In ICMR, 2014.

Digital Library

[7]

M. Fischer, H. K. Ekenel, and R. Stiefelhagen. Person re-identification in TV series using robust face recognition and user feedback. Multimedia Tools and Applications, 2010.

Digital Library

[8]

X. Geng, Z.-H. Zhou, and K. Smith-Miles. Automatic Age Estimation Based on Facial Aging Patterns. T-PAMI, 29(12):2234--2240, 2007.

Digital Library

[9]

D. Gong, Z. Li, D. Lin, J. Liu, and X. Tang. Hidden Factor Analysis for Age Invariant Face Recognition. In ICCV, 2013.

Digital Library

[10]

G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Technical Report 07--49, Univ. of Massachusetts, Amherst, 2007.

[11]

B. Klare and A. K. Jain. Face recognition across time lapse: On learning feature subspaces. In Intl. Joint Conf. on Biometrics, 2011.

Digital Library

[12]

A. Lanitis, C. Draganova, and C. Christodoulou. Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern: B Cybern, 34(1):621--628, Feb 2004.

Digital Library

[13]

Y. Li, R. Wang, Z. Cui, S. Shan, and X. Chen. Compact Video Code and Its Application to Robust Face Retrieval in TV-Series. In BMVC, 2014.

[14]

Z. Li, U. Park, and A. K. Jain. A Discriminative Model for Age Invariant Face Recognition. IEEE Trans on Inf. Forensics Security, 6(3), 2011.

Digital Library

[15]

H. Ling, S. Soatto, N. Ramanathan, and D. Jacobs. Face Verification Across Age Progression Using Discriminative Methods. IEEE Trans on Inf. Forensics Security, 5(1):82--91, 2010.

Digital Library

[16]

E. G. Ortiz, A. Wright, and M. Shah. Face Recognition in Movie Trailers via Mean Sequence Sparse Representation-based Classification. In CVPR, 2013.

Digital Library

[17]

U. Park, Y. Tong, and A. Jain. Age-Invariant Face Recognition. T-PAMI, 32(5):947--954, May 2010.

Digital Library

[18]

O. M. Parkhi, K. Simonyan, A. Vedaldi, and A. Zisserman. A Compact and Discriminative Face Track Descriptor. In CVPR, 2014.

Digital Library

[19]

N. Ramanathan and R. Chellappa. Modeling Age Progression in Young Faces. In CVPR, 2006.

Digital Library

[20]

K. Ricanek and T. Tesafaye. Morph: A longitudinal image database of normal adult age-progression. In IEEE FG, 2006.

Digital Library

[21]

S. Setty and et al. Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations. In NCVPRIPG, 2013.

[22]

J. Sivic, M. Everingham, and A. Zisserman. Person spotting: video shot retrieval for face sets. In CIVR, 2005.

Digital Library

[23]

L. Wolf, T. Hassner, and I. Maoz. Face Recognition in Unconstrained Videos with Matched Background Similarity. In CVPR, 2011.

Digital Library

Cited By

Somandepalli KHebbar RNarayanan S(2022)Robust Character Labeling in Movie Videos: Data Resources and Self-Supervised Feature AdaptationIEEE Transactions on Multimedia10.1109/TMM.2021.309615524(3355-3368)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3096155
Somandepalli KGuha TMartinez VKumar NAdam HNarayanan S(2021)Computational Media Intelligence: Human-Centered Machine Analysis of MediaProceedings of the IEEE10.1109/JPROC.2020.3047978109:5(891-910)Online publication date: May-2021
https://doi.org/10.1109/JPROC.2020.3047978
Brown AKalogeiton VZisserman A(2021)Face, Body, Voice: Video Person-Clustering with Multiple Modalities2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW54120.2021.00357(3177-3187)Online publication date: Oct-2021
https://doi.org/10.1109/ICCVW54120.2021.00357
Show More Cited By

Index Terms

Accio: A Data Set for Face Track Retrieval in Movies Across Age
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Collaborative expression representation using peak expression and intra class variation face images for practical subject-independent emotion recognition in videos

This paper proposes a facial expression recognition (FER) method in videos. The proposed method automatically selects the peak expression face from a video sequence using closeness of the face to the neutral expression. The severely non-frontal faces ...
Open-set face recognition across look-alike faces in real-world scenarios

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face ...
Face recognition under varying illumination using gradientfaces

In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the Gradientfaces. Theoretical analysis shows Gradientfaces is an illumination insensitive measure, and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Copyright © 2015 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 the author(s) 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].

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Short-paper

Funding Sources

CHIST-ERA
Deutsche Forschungsgemeinschaft

Conference

ICMR '15

Sponsor:

SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

Acceptance Rates

ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
127
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Somandepalli KHebbar RNarayanan S(2022)Robust Character Labeling in Movie Videos: Data Resources and Self-Supervised Feature AdaptationIEEE Transactions on Multimedia10.1109/TMM.2021.309615524(3355-3368)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3096155
Somandepalli KGuha TMartinez VKumar NAdam HNarayanan S(2021)Computational Media Intelligence: Human-Centered Machine Analysis of MediaProceedings of the IEEE10.1109/JPROC.2020.3047978109:5(891-910)Online publication date: May-2021
https://doi.org/10.1109/JPROC.2020.3047978
Brown AKalogeiton VZisserman A(2021)Face, Body, Voice: Video Person-Clustering with Multiple Modalities2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW54120.2021.00357(3177-3187)Online publication date: Oct-2021
https://doi.org/10.1109/ICCVW54120.2021.00357
Sharma VTapaswi MSarfraz MStiefelhagen R(2020)Video Face Clustering With Self-Supervised Representation LearningIEEE Transactions on Biometrics, Behavior, and Identity Science10.1109/TBIOM.2019.29472642:2(145-157)Online publication date: Apr-2020
https://doi.org/10.1109/TBIOM.2019.2947264
Sharma VTapaswi MSarfraz MStiefelhagen R(2020)Clustering based Contrastive Learning for Improving Face Representations2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)10.1109/FG47880.2020.00011(109-116)Online publication date: 16-Nov-2020
https://dl.acm.org/doi/10.1109/FG47880.2020.00011
Röthlingshöfer VSharma VStiefelhagen RAmsaleg LHuet BLarson MGravier GHung HNgo CTsang Ooi W(2019)Self-supervised Face-Grouping on GraphsProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3351071(247-256)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3343031.3351071
Sharma VTapaswi MSarfraz MStiefelhagen R(2019)Self-Supervised Learning of Face Representations for Video Face Clustering2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)10.1109/FG.2019.8756609(1-8)Online publication date: 14-May-2019
https://dl.acm.org/doi/10.1109/FG.2019.8756609
He YCao KLi CLoy CMcIlraith SWeinberger K(2018)Merge or not? learning to group faces via imitation learningProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504880(6902-6909)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.5555/3504035.3504880
Goncalves GCarlos Nazare ADiniz MEduardo Coelho Lima LSchwartz W(2018)AVSS Challenges 2018 Soft Biometric Retrieval Using Deep Multi-Task Network2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS.2018.8639325(1-6)Online publication date: Nov-2018
https://doi.org/10.1109/AVSS.2018.8639325
Qiao SWang RShan SChen X(2017)Deep Video Code for Efficient Face Video RetrievalComputer Vision – ACCV 201610.1007/978-3-319-54187-7_20(296-312)Online publication date: 11-Mar-2017
https://doi.org/10.1007/978-3-319-54187-7_20
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.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten