Application of Spectral Information in Identification of Real-Fake Face Images
Pages 205 - 208
Abstract
The biometric authentication systems based on face recognition are easy to implement with any device, which has an in-built camera. These systems are very secure, but with various attacking methods, such security also becomes vulnerable. Among various types of possible attacks, spoofing is an attack in which available face information is presented before the sensor to mislead the authentication system. In this paper, a model has been presented based on spectral analysis of the captured images to classify them as real faces and face images. We consider Fourier and cosine transform of the image for evaluation of various Image Quality Measures (IQMs), with which the classification is performed using neural networks. The proposed spectral contents based IQMs are compared with several conventional IQMs to judge their performance. The simulation on Replay-Attack database has shown the improvement in the performance of the present model.
References
[1]
I. Avcibas, B. Sankur, and K. Sayood. Statistical evaluation of image quality measures. Electronic Imaging, 11(2):206--223, Apr. 2002.
[2]
S. Bayram, İ. Avcıbaş, B. Sankur, and N. Memon. Image manipulation detection. Journal of Electronic Imaging, 15(4):041102--041102, 2006.
[3]
I. Chingovska, A. Anjos, and S. Marcel. On the Effectiveness of Local Binary Patterns in Face Anti-spoofing. In IEEE BIOSIG 2012, Sept. 2012.
[4]
A. M. Eskicioglu and P. S. Fisher. Image quality measures and their performance. Communications, IEEE Transactions on, 43(12):2959--2965, 1995.
[5]
J. Galbally, S. Marcel, and J. Fierrez. Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. Image Processing, IEEE Transactions on, 23(2):710--724, 2014.
[6]
Q. Huynh-Thu and M. Ghanbari. Scope of validity of PSNR in image/video quality assessment. Electronics letters, 44(13):800--801, 2008.
[7]
A. K. Jain, K. Nandakumar, and A. Nagar. Biometric template security. EURASIP Journal on Advances in Signal Processing, 2008:113, 2008.
[8]
J. Li, Y. Wang, T. Tan, and A. K. Jain. Live face detection based on the analysis of fourier spectra. In Defense and Security, pages 296--303. International Society for Optics and Photonics, 2004.
[9]
K. A. Nixon, V. Aimale, and R. K. Rowe. Spoof detection schemes. In Handbook of biometrics, pages 403--423. Springer, 2008.
[10]
G. Pan, L. Sun, Z. Wu, and S. Lao. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1--8. IEEE, 2007.
[11]
G. Pan, L. Sun, and Z. Wu. Liveness detection for face recognition. INTECH Open Access Publisher, 2008.
[12]
H. A. Rowley, S. Baluja, and T. Kanade. Rotation invariant neural network-based face detection. In Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on, pages 38--44. IEEE, 1998.
[13]
M. C. Stamm and K. R. Liu. Forensic detection of image manipulation using statistical intrinsic fingerprints. Information Forensics and Security, IEEE Transactions on, 5(3):492--506, 2010.
[14]
X. Tan, Y. Li, J. Liu, and L. Jiang. Face liveness detection from a single image with sparse low rank bilinear discriminative model. In Computer Vision--ECCV 2010, pages 504--517. Springer, 2010.
[15]
S. Yao, W. Lin, E. Ong, and Z. Lu. Contrast signal-to-noise ratio for image quality assessment. In Image Processing, 2005. ICIP 2005. IEEE International Conference on, volume 1. IEEE, 2005.
Index Terms
- Application of Spectral Information in Identification of Real-Fake Face Images
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Published In
September 2015
481 pages
ISBN:9781450335522
DOI:10.1145/2818567
Copyright © 2015 ACM.
© 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Association for Computing Machinery
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Publication History
Published: 25 September 2015
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ICCCT '15
ICCCT '15: Sixth International Conference on Computer and Communication Technology 2015
September 25 - 27, 2015
Allahabad, India
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