research-article

Towards face unlock: on the difficulty of reliably detecting faces on mobile phones

Authors:

Rainhard D. Findling,

Rene MayrhoferAuthors Info & Claims

MoMM '12: Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia

Pages 275 - 280

https://doi.org/10.1145/2428955.2429008

Published: 03 December 2012 Publication History

Abstract

Currently, reliable face detection and recognition are becoming more important on mobile devices -- e.g. to unlock the screen. However, using only frontal face images for authentication purposes can no longer be considered secure under the assumption of easy availability of frontal snapshots of the respective device owners from social networks or other media. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures. In this paper, we analyze current methods to face detection and recognition regarding their usability in the mobile domain, and then propose an approach to a Face Unlock system on a smart phone intended to be more secure than current approaches while still being convenient to use: we use both frontal and profile face information available during a pan shot around the user's head, by combining camera images and movement sensor data. Current results to face detection are promising, but reliable face recognition needs further research.

References

[1]

W. Bao, H. Li, N. Li, and W. Jiang. A liveness detection method for face recognition based on optical flow field. In Image Analysis and Signal Processing, 2009. IASP 2009. International Conference on, pages 233--236, Apr. 2009.

[2]

P. N. Belhumeur, J. a. P. Hespanha, and D. J. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711--720, July 1997.

Digital Library

[3]

D. Douxchamps and N. Campbell. Robust real time face tracking for the analysis of human behaviour. In A. Popescu-Belis, S. Renals, and H. Bourlard, editors, Machine Learning for Multimodal Interaction, volume 4892 of Lecture Notes in Computer Science, pages 1--10. Springer Berlin / Heidelberg, 2008.

Digital Library

[4]

R. Frischholz and A. Werner. Avoiding replay-attacks in a face recognition system using head-pose estimation. In Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on, pages 234--235, Oct. 2003.

Digital Library

[5]

A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence, 23(6):643--660, 2001.

Digital Library

[6]

R. Hsu, M. Abdel-Mottaleb, and A. K. Jain. Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:696--706, 2002.

Digital Library

[7]

A. K. Jain and S. Z. Li. Handbook of Face Recognition. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2005.

Digital Library

[8]

W. Kurutach, R. Fooprateepsiri, and S. Phoomvuthisarn. A highly robust approach face recognition using hausdorff-trace transformation. In ICONIP (2), pages 549--556, 2010.

Digital Library

[9]

R. Lienhart and J. Maydt. An extended set of haar-like features for rapid object detection. In IEEE ICIP 2002, pages 900--903, 2002.

[10]

C. Liu. A bayesian discriminating features method for face detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(6): 725--740, june 2003.

Digital Library

[11]

B. Martinkauppi. Face colour under varying illumination - analysis and applications. PhD thesis, University of Oulu, 2002.

[12]

A. Pentland, B. Moghaddam, and T. Starner. View-based and modular eigenspaces for face recognition. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on, pages 84--91, June 1994.

[13]

H. A. Rowley, S. Member, S. Baluja, and T. Kanade. Neural network-based face detection. IEEE Transactions On Pattern Analysis and Machine intelligence, 20:23--38, 1998.

Digital Library

[14]

H. Sahoolizadeh, D. Sarikhanimoghadam, and H. Dehghani. Face detection using gabor wavelets and neural networks. World Academy of Science, Engineering and Technology 45, 2008.

[15]

F. Samaria and A. Harter. Parameterisation of a stochastic model for human face identification. In Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on, pages 138--142, Dec. 1994.

[16]

M. C. Santana, O. Déniz-Suárez, L. Antón-Canalís, and J. Lorenzo-Navarro. Face and facial feature detection evaluation - performance evaluation of public domain haar detectors for face and facial feature detection. In A. Ranchordas and H. AraÃžjo, editors, VISAPP (2), pages 167--172. INSTICC - Institute for Systems and Technologies of Information, Control and Communication, 2008.

[17]

H. Schneiderman and T. Kanade. Object detection using the statistics of parts. International Journal of Computer Vision, 56(3):151--177, Feb. 2004.

Digital Library

[18]

K. Sung and T. Poggio. Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:39--51, 1998.

Digital Library

[19]

R. Tronci, D. Muntoni, G. Fadda, M. Pili, N. Sirena, G. Murgia, M. Ristori, and F. Roli. Fusion of multiple clues for photo-attack detection in face recognition systems. In Biometrics (IJCB), 2011 International Joint Conference on, pages 1--6, Oct. 2011.

Digital Library

[20]

M. Turk and A. Pentland. Eigenfaces for recognition. Cognitive Neuroscience, 3(1):71--86, Jan. 1991.

Digital Library

[21]

P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. Proceedings CVPR, 1:511--518, 2001.

[22]

M. Wagner and G. Chetty. Liveness assurance in face authentication. In S. Z. Li and A. Jain, editors, Encyclopedia of Biometrics, pages 908--916. Springer US, 2009.

[23]

H. Wechsler. Reliable Face Recognition Methods: System Design, Implementation and Evaluation (International Series on Biometrics). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.

Digital Library

[24]

L. Wiskott, J.-M. Fellous, N. Krüger, and C. V. D. Malsburg. Face recognition by elastic bunch graph matching. IEEE transactions on pattern analysis and machine intelligence, 19:775--779, 1997.

Digital Library

[25]

A. L. Yuille, P. W. Hallinan, and D. S. Cohen. Feature extraction from faces using deformable templates. International Journal of Computer Vision, 8(2):99--111, Aug. 1992.

Digital Library

[26]

B. D. Zarit, B. J. Super, and F. K. H. Quek. Comparison of five color models in skin pixel classification. In International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pages 58--63, 1999.

Digital Library

[27]

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Comput. Surv., 35(4):399--458, Dec. 2003.

Digital Library

[28]

X. Zou, J. Kittler, and K. Messer. Illumination invariant face recognition: A survey. In Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007. First IEEE International Conference on, pages 1--8, Sept. 2007.

Cited By

Grazian CJin QTangari G(2024)Assessing the invertibility of deep biometric representations: Investigating CNN hyperparameters for enhanced security against adversarial attacksExpert Systems with Applications10.1016/j.eswa.2024.125848(125848)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125848
Fu HLi WWang Y(2024)PassFile: Graphical Password Authentication Based on File Browsing RecordsMachine Learning for Cyber Security10.1007/978-981-97-2458-1_3(28-43)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-981-97-2458-1_3
Deng PGe CQiao XWei HSun Y(2023)Attention-Aware Dual-Stream Network for Multimodal Face Anti-SpoofingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329342318(4258-4271)Online publication date: 2023
https://doi.org/10.1109/TIFS.2023.3293423
Show More Cited By

Index Terms

Towards face unlock: on the difficulty of reliably detecting faces on mobile phones
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Recommendations

A New Hybrid Approach Using PCA for Pose Invariant Face Recognition

In this paper a new hybrid approach using PCA for pose invariant face recognition is proposed. In this proposed approach three algorithms are combined to make a new hybrid approach. The first step is to detect face and its part. It is done by well known ...
Face Localization in 2D Frontal Face Images Using Luminosity Profiles Analysis
Multimodal Signals: Cognitive and Algorithmic Issues

Face detection from a single image represents a challenging task because of variability in scale, location, orientation and pose. Facial expression, occlusion, and lighting conditions also change the overall appearance of faces. In this work we propose ...
Real-time face detection and recognition using hybrid-information extracted from face space and facial features

This paper describes both face detection using the eigenface space and face recognition using neural networks. Real-time face detection from face images was performed in two steps. In the first step, a normalized skin color map based on the Gaussian ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

MoMM '12: Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia

December 2012

323 pages

ISBN:9781450313070

DOI:10.1145/2428955

Conference Chair:
Matthias Steinbauer
Johannes Kepler University Linz, Austria
,
Editor:
Ismail Khalil
Johannes Kepler University Linz, Austria
,
General Chair:
Eric Pardede
La Trobe University, Australia
,
Program Chair:
David Taniar
Monash University, Australia

Copyright © 2012 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]

Sponsors

@WAS: International Organization of Information Integration and Web-based Applications and Services

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MoMM '12

Sponsor:

@WAS

MoMM '12: The 10th International Conference on Advances in Mobile Computing & Multimedia

December 3 - 5, 2012

Bali, Indonesia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
522
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Grazian CJin QTangari G(2024)Assessing the invertibility of deep biometric representations: Investigating CNN hyperparameters for enhanced security against adversarial attacksExpert Systems with Applications10.1016/j.eswa.2024.125848(125848)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125848
Fu HLi WWang Y(2024)PassFile: Graphical Password Authentication Based on File Browsing RecordsMachine Learning for Cyber Security10.1007/978-981-97-2458-1_3(28-43)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-981-97-2458-1_3
Deng PGe CQiao XWei HSun Y(2023)Attention-Aware Dual-Stream Network for Multimodal Face Anti-SpoofingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329342318(4258-4271)Online publication date: 2023
https://doi.org/10.1109/TIFS.2023.3293423
Trad FHussein AChehab A(2022)Free Text Keystroke Dynamics-based Authentication with Continuous Learning: A Case Study2022 IEEE 21st International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS)10.1109/IUCC-CIT-DSCI-SmartCNS57392.2022.00031(125-131)Online publication date: Dec-2022
https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS57392.2022.00031
Li WTan JZhu N(2022)Double-X: Towards Double-Cross-Based Unlock Mechanism on SmartphonesICT Systems Security and Privacy Protection10.1007/978-3-031-06975-8_24(412-428)Online publication date: 3-Jun-2022
https://doi.org/10.1007/978-3-031-06975-8_24
Li WWang YTan JZhu N(2021)DCUS: Evaluating Double-Click-Based Unlocking Scheme on SmartphonesMobile Networks and Applications10.1007/s11036-021-01842-127:1(382-391)Online publication date: 27-Oct-2021
https://doi.org/10.1007/s11036-021-01842-1
Li WTan JZhu NWang Y(2021)Designing Double-Click-Based Unlocking Mechanism on SmartphonesSecurity, Privacy, and Anonymity in Computation, Communication, and Storage10.1007/978-3-030-68884-4_47(573-585)Online publication date: 7-Feb-2021
https://doi.org/10.1007/978-3-030-68884-4_47
Cao HChang K(2019)Nonintrusive Smartphone User Verification Using Anonymized Multimodal DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.282830931:3(479-492)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/TKDE.2018.2828309
Cardona López APineda Torres F(2018)Reconocimiento de rostros en tiempo real sobre dispositivos móviles de bajo costoLámpsakos10.21501/21454086.2938(30-39)Online publication date: 3-Jul-2018
https://doi.org/10.21501/21454086.2938
Siddiqui TBharadwaj SDhamecha TAgarwal AVatsa MSingh RRatha N(2016)Face anti-spoofing with multifeature videolet aggregation2016 23rd International Conference on Pattern Recognition (ICPR)10.1109/ICPR.2016.7899772(1035-1040)Online publication date: Dec-2016
https://doi.org/10.1109/ICPR.2016.7899772
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