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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

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2083-2567
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Temporal Analysis Of Adaptive Face Recognition

Zahid Akhtar / Ajita Rattani / Gian Luca Foresti
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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

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

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

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

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

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

  • [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.Google Scholar

  • [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 ScienceGoogle Scholar

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

  • [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.Google Scholar

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

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

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

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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 ScienceCrossrefGoogle Scholar

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

  • [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.Google Scholar

  • [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.CrossrefGoogle Scholar

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

  • [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.Google Scholar

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

  • [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.Google Scholar

  • [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 ScienceGoogle Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

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.

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

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