Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

Open Access
Online
ISSN
2083-2567
See all formats and pricing
In This Section

Temporal Analysis Of Adaptive Face Recognition

Zahid Akhtar
  • Dept. of Mathematics and Computer Science, University of Udine, Italy.
  • Email:
/ Ajita Rattani
  • Dept. of Computer Science and Electrical Engineering, University of Missouri at Kansas City, USA.
  • Email:
/ Gian Luca Foresti
  • Dept. of Mathematics and Computer Science, University of Udine, Italy.
  • Email:
Published Online: 2015-03-01 | DOI: https://doi.org/10.1515/jaiscr-2015-0012

Abstract

Aging has profound effects on facial biometrics as it causes change in shape and texture. However, aging remains an under-studied problem in comparison to facial variations due to pose, illumination and expression changes. A commonly adopted solution in the state-of-the-art is the virtual template synthesis for aging and de-aging transformations involving complex 3D modelling techniques. These methods are also prone to estimation errors in the synthesis. Another viable solution is to continuously adapt the template to the temporal variation (ageing) of the query data. Though efficacy of template update procedures has been proven for expression, lightning and pose variations, the use of template update for facial aging has not received much attention so far. Therefore, this paper first analyzes the performance of existing baseline facial representations, based on local features, under ageing effect then investigates the use of template update procedures for temporal variance due to the facial age progression process. Experimental results on FGNET and MORPH aging database using commercial VeriLook face recognition engine demonstrate that continuous template updating is an effective and simple way to adapt to variations due to the aging process.

References

  • [1] Zahid Akhtar, Security of Multimodal Biometric Systems against Spoof Attacks, PhD thesis, University of Cagliari, Italy, 2012.

  • [2] N. Poh, J. Kittler and A. Rattani, and M. Tistarelli, Group-specific score normalization for biometric systems, Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38-45, 2010.

  • [3] Z. Akhtar, S. Kale and N. Alfarid, Spoof Attacks on Multimodal Biometric Systems, Proc. Int'l Conference on Information and Network Technology (ICINT), pp. 46-51, 2011.

  • [4] Z. Akhtar, C. Micheloni, C. Piciarelli, G. L. Foresti, MoBio LivDet: Mobile Biometric Liveness Detection, IEEE Int'l Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 187-192, 2014.

  • [5] Z. Akhtar and N. Alfarid, Secure Learning Algorithm for Multimodal Biometric Systems against Spoof Attacks, Proc. Int'l Conference on Information and Network Technology (ICINT), pp. 52-57, 2011.

  • [6] Z. Akhtar, C. Micheloni and G. L. Foresti, Biometric Liveness Detection: Challenges and Open Research Opportunities, IEEE Security & Privacy, 2015.

  • [7] FRVT 2013, http://www.nist.gov/itl/iad/ig/frvt-2013.cfm/

  • [8] A. Tsymbal, The problem of concept drift: Definitions and related work, Department of Computer Science, Trinity College, Ireland, 2004.

  • [9] Z. Akhtar, A. Rattani, A. Hadid and M. Tistarelli, Face Recognition under Ageing Effect: A Comparative Analysis, Proc. Int'l Conf. on Image Analysis and Processing (ICIAP), pp. 309-318, 2013.

  • [10] P. J. Flynn, K. W. Bowyer and P. J. Phillips, Assessment of time dependency in face recognition: An initial study, Proc. 4th Int. Conf. on Audio and Video based Biometric Person Authentication, pp.44-51, 2003.

  • [11] H. Ling, S. Soatto, N. Ramanathan and D. W. Jacobs, A study of face recognition as people age, Proc. 11th IEEE Int. Conf. on Computer Vision (ICCV), pp. 1-8, 2007.

  • [12] A. Rattani, B. Freni, G. L. Marcialis and F. Roli, Template Update Methods in Adaptive Biometric Systems: A Critical Review, Proc. International Conference on Biometrics (ICB), pp. 847-857, 2009.

  • [13] Z. Akhtar, A. Ahmed, C. E. Erdem and G. L. Foresti, Biometric Template Update under Facial Aging, IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, 2014.

  • [14] A. Lanitis, C. J. Taylor and T. F. Cootes, Toward automatic simulation of aging effects on face images, IEEE Tran. on Pattern Analysis and Machine Intelligence, 24(4):442-455, 2002.

  • [15] N. Ramanathan and R. Chellappa, Face verification across age progression, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 462-469, 2005.

  • [16] N. Ramanathan and R. Chellappa, Modeling age progression in young faces, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 387-394, 2006.

  • [17] U. Park, Y. Tong and A. K. Jain, Age-invariant face recognition, IEEE Tran. on Pattern Analysis and Machine Intelligence, 32(5):947-954, 2010. [Web of Science]

  • [18] FGNET Aging Database, http://www.fgnet.rsunit.com/

  • [19] K. J. Ricanek and T. Tesafaye, Morph: A longitudinal image database of normal adult ageprogression, Proc. Int'l Conf. on Automatic Face and Gesture Recognition (FG), pp. 341-345, 2006.

  • [20] N. Nixon and P. Galassi, The brown sisters, thirtythree years, In The Museum of Modern Art, NY, USA, 2007.

  • [21] A. Rattani. Adaptive Biometric System based on Template Update Procedures, PhD thesis, University of Cagliari, Italy, 2010.

  • [22] N. Poh, A. Rattani and F. Roli, Critical Analysis of Adaptive Biometric Systems, IET Biometrics, 1(4):179-187, 2012. [Web of Science]

  • [23] A. Rattani and A. Ross, Automatic Adaptation of Fingerprint Liveness Detector to New Spoof Materials, In Proc. IEEE International Joint Conference on Biometrics (IJCB), 2014.

  • [24] X. Liu, T. Chen, and S. M. Thornton, Eigenspace updating for non-stationary process and its application to face recognition, Pattern Recognition, pp. 1945-1959, 2003.

  • [25] S. K. Pavani, F. M. Sukno, C. Butakoff, X. Planes and A. F. Frangi, A confidence based update rule for self-updating human face recognition systems, Proc. Int'l Conf. on Biometrics (ICB), pp. 151-160, 2009.

  • [26] A. Rattani, G.L. Marcialis and F. Roli, Biometric template update using the graph mincut: a case study in face verification. Proc. 6th IEEE Biometric Symposium, 2008.

  • [27] N. Poh, J. Kittler, S. Marcel, D. Matrouf and J. F. Bonastre, Model and Score Adaptation for Biometric Systems: Coping With Device Interoperability and Changing Acquisition Conditions, Proc 20th Int'l Conf. on Pattern Recognition (ICPR), pp. 1229-1232, 2010.

  • [28] A. Franco, D. Maio and D. Maltoni, Incremental template updating for face recognition in home environments, Pattern Recognition, 43:2891-2903, 2010. [Web of Science] [Crossref]

  • [29] X. Jiang and W. Ser, Online fingerprint template improvement, IEEE Tran. PAMI, vol. 8, pp. 1121-1126, 2002.

  • [30] C. Ryu, K. Hakil and A. Jain, Template adaptation based fingerprint verification, Proc. Int'l Conf. on Pattern Recognition (ICPR), pp. 582-585, 2006.

  • [31] U. Uludag, A. Ross and A. Jain, Biometric template selection and update: a case study in fingerprints, Pattern Recognition, 37(7):1533-1542, 2004. [Crossref]

  • [32] F. Roli and G.L Marcialis, Semi-supervised pcabased face recognition using self training, Proc. Int'l workshop on S+SSPR, 2006.

  • [33] Verilook: http://www.neurotechnology.com/

  • [34] T. Ahonen, A. Hadid and M. Pietikainen, Face description with local binary patterns: application to face recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp.2037-2041, 2006.

  • [35] Chi-Ho Chan, J. Kittler and K. Messer, Multi-scale Local Binary Pattern Histograms for Face Recognition, In ICB, pp. 809-818, 2007.

  • [36] T. Ahonen, E. Rahtu and V. Ojansivu and J. Heikkil, Recognition of blurred faces using local phase quantization, In Proc. Int. Conf. on Patt.Reco., pp. 8-11, 2008.

  • [37] X. Tan and B. Triggs, Enhanced Local TextureFeature Sets for Face Recognition under Difficult Lighting Conditions, IEEE Trans. on Image Processing, vol. 19, no. 6, pp. 1635-1650, 2010. [Web of Science]

  • [38] L. Wiskott, J.M. Fellous, N. Kruger and C. Malsburg, Face recognition by elastic bunch graph matching, IEEE Trans. on PAMI, vol. 19, no. 7, pp. 775-780, 1997.

  • [39] D. R. Kisku, A. Rattani, E. Grosso and M. Tistarelli, Face Identification by SIFT-based Complete Graph Topology, In Proc. of 5th IEEE Int'l Workshop on Automatic Identification Advanced Technologies, pp. 63-68, 2007.

  • [40] P. Dreuw, P. Steingrube and H. Hanselmann and H. Ney, SURF-Face: Face Recognition Under Viewpoint Consistency Constraints, In Proc. BMVC, pp. 1-11, 2009.

  • [41] A. Rattani, G. L. Marcialis, F. Roli, An Experimental Analysis of the Relationship between Biometric Template Update and the Doddington's Zoo in Face Verification, In Proc. of 14th Int'l Conference on Image Analysis and Processing, 2009.

  • [42] Z. Akhtar, G. Fumera, G. L. Marcialis and F. Roli, Evaluation of Multimodal Biometric Score Fusion Rules under Spoof Attacks, 5th IAPR Int'l Conference on Biometrics (ICB), pp. 402-407, 2012.

About the article

Published Online: 2015-03-01

Published in Print: 2014-10-01



Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0012. Export Citation

© Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

Comments (0)

Please log in or register to comment.
Log in