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Adaptive Regression Splines Models for Predicting Facial Image Verification and Quality Assessment Scores

Year 2015, Volume: 3 Issue: 1, 17 - 26, 27.02.2015

Abstract

— Many biometric applications are faced with enormous performance challenges due to submission of low quality facial images. In this study, adaptive regression splines (ARES) models were built for predicting algorithm matching scores (AMS) and overall quality scores (OQS). A face verification and image quality assessment (FVIQA) framework was adopted to extract five facial quality features from still images. The SCface database was adopted for the training and testing datasets with 2,093 and 897 images respectively. ARES models were built from the normalized individual quality scores and algorithm matching scores using ARESLab in the MATLAB environment. A black face surveillance camera (BFSC) database of 50 subjects was populated to mimic the SCface database and act as the target dataset for the model validation. Results from the study shows that FVIQA quality scores and other experimental results are comparable and consistent with previous research works. The model ANOVA decomposition showed that pose variation is the major determinant for model OQS and AMS with 0.046 and 0.261 respectively. From the performance evaluation, model OQS achieved 99.96% and 99.81% prediction accuracy on the test and target datasets while model AMS achieved 87.04% and 84.73% respectively. Subsequently, no failure-to-acquire (FTA) was recorded when superior face images were selected from the SCface database using the developed image verification and quality assessment (IVQA) number

References

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  • S. Z. Li and A. K. Jain (Eds.), “Handbook of Face Recognition”. Springer- Verlag, Secaucus, New York, USA, 2005.
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  • P. Grother and E. Tabassi, “Performance of Biometric Quality Measures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):531-543, 2006.
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  • N. Ozay, Y. Tong, F. Wheeler and X. Liu, “Improving face recognition with a quality-based probabilistic framework”, in Proc. Computer Vision and Pattern Recognition (CVPR) conference 2009.
  • W. J. Scheirer, A. Bendale and T. E. Boult, “Predicting biometric facial recognition failure with similarity surfaces and support vector machines”, in Proc. of IEEE Computer Society Workshop on Biometrics, 2008.
  • M. T. Chan, R. Brown and W. Turner, “Incorporating quality metrics in multimodal biometric fusion”, in Proc. of IEEE Computer Society Workshop on Biometrics, pp 67-79, 2006.
  • K. Nandakumar, Y. Chen, A. K. Jain and S. C. Dass, “Quality-based score level fusion in multibiometric systems”, In International Conference on Pattern Recognition, 2006.
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  • E. Tabassi, “NIST Fingerprint image quality and relation to PIV”, NIST Image Group Technical report, 2005.
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  • E. Tabassi, C. L. Wilson and C. I. Watson, “Fingerprint Image Quality”, Technical Report NISTIR 7151, NIST, 2004.
  • P. Liao, H. Lin, P. Zeng, S. Bai, H. Ma, S. Ding, “Facial Image Quality Assessment Based on Support Vector Machines”, in Proc. of International Conference on Biomedical Engineering and Biotechnology (iCBEB), pp. 810-813, 2012.
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  • C. Sasivarnan, A. Jagan, J. Kaur, D. Jyoti, D. S. Rao. “Image Quality Assessment Techniques on Spatial Domain”, IJCST, 2(3):177-184, 2011.
  • E. O. Omidiora, S. O. Olabiyisi, J. A. Ojo, R. A. Ganiyu, and A. Abayomi- Alli, “Enhanced Face Verification and Image Quality Assessment Scheme Using Modified Optical Flow Technique”, in Proc. WCECS’14 International Conference on Signal Processing and Imaging Engineering (ICSPIE'14), San Francisco, USA, 22nd-24th October, 2014.
  • R. Wallace, M. McLaren, C. McCool and S. Marcel, “Inter-session Variability Modelling and Joint Factor Analysis for Face Authentication”, in Proc. International Joint Conference on Biometrics (IJCB), 11-13 October, Arlington, Virginia, USA, pp 1-8, 2011.
  • J. H. Friedman, “Multivariate adaptive regression splines”, Annals of Statistics, 19(1):1-141, 1991.
  • P.J. Nieto, F. S. Lasheras, F. J. Juez, and J. R. Fernández, “Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain)”, Journal of Hazard Matter, 195 (2011): 414–421, 2011.
  • G. Jekabsons, “ARESLab: Adaptive Regression Splines toolbox for Matlab/Octave”, http://www.cs.rtu.lv/jekabsons/ Available online at
  • P. Craven and G. Wahba, “Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation,” Numer Math, Vol. 31:317- 403, 1979.
  • X. H. Chen and C. Z. Li, “Image quality assessment model based on features and applications in face recognition”, in Proc. IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1-4, 2011.
  • E. O. Omidiora, S. O. Olabiyisi, J. A. Ojo, A. Abayomi-Alli, F. Izilein, and P. I. Ezomo, "MACE Correlation Filter Algorithm for Face Verification in Surveillance Scenario", IJCSE, USA, 13(1):6-15, 2013.
  • E. O. Omidiora, S. O. Olabiyisi, J. A. Ojo, A. Abayomi-Alli, O. Abayomi- Alli, K. B. Erameh, “Facial Image Verification and Quality Assessment System – FaceIVQA”, International Journal of Electrical and Computer Engineering (IJECE), Malaysia, Vol. 3(6), 863-874, 2013.
  • A. Bouzerdoum, A. Havstad and A. Beghdadi, “Image quality assessment using a neural network approach”, Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, pp 330-333, 2004.
  • S. Bharadwaj, H. Bhatt, M. Vatsa, R. Singh and A. Noore, “Quality assessment based denoising to improve face recognition performance”, Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 140-145, 2011.
Year 2015, Volume: 3 Issue: 1, 17 - 26, 27.02.2015

Abstract

References

  • K. Delac and M. Grgic (Eds.), “Face Recognition”, I-Tech Education and Publishing, Vienna, July 2007.
  • S. Z. Li and A. K. Jain (Eds.), “Handbook of Face Recognition”. Springer- Verlag, Secaucus, New York, USA, 2005.
  • K. Delac and M. Grgic, “A Survey of Biometric Recognition Methods”, Proc. of the 46th International Symposium Electronics in Marine, ELMAR-2004, Zadar, Croatia, pp. 184-193, 2004.
  • W. Zhao, R. Chellappa, A. Rosenfeld, P. J Phillips, “Face Recognition: A Literature Survey”, ACM Computing Surveys, 35(4):399-458, 2003.
  • H. Ekenel and B. Sankur, “Feature Selection in the Independent Component Subspace for Face recognition”, Pattern Recognition Letters, 25(12):1377-1388, 2004.
  • N. Poh, J. kittler, S. Marcel, D. Matrouf, J. Bonastre, “Model and Score Adaptation for Biometric Systems: Coping with Device Interoperability and changing Acquisition Conditions”, International Conference on Pattern Recognition, IEEE Computer Society, 2010.
  • U. Park, “Face Recognition: Face in Video, Age Invariance, and Facial Marks”, An Unpublished Ph.D Dissertation submitted to the Department of Computer Science, Michigan State University, U.S.A, 2009.
  • F. Perronnin, “A Probabilistic Model of Face Mapping Applied to Person Recognition”, An Unpublished Ph.D Thesis submitted to the Department of Multi-Media Communications, École Polytechnique Federale Lausanne (EPFL), France, 2004.
  • D. P. D’Amato, “Best practices for taking face photographs and face image quality Metrics”, NIST Biometric Quality Workshop, March 2006.
  • P. Grother and E. Tabassi, “Performance of Biometric Quality Measures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):531-543, 2006.
  • F. Hua, P. Johnson, N. Sazonova, P. Lopez-Meyer, S. Schuckers, ”Impact of Out-of-Focus Blur on Face Recognition Performance Based on Modular Transfer Function”, International Conference on Biometrics, 2012. pp 256-272, 2012.
  • M. Grgic, K. Delac, and S. Grgic, “SCface - Surveillance Cameras Face Database”, Multimed Tools Application, 51:863–879, 2011. Available online at http://www.scface.org/
  • N. Ozay, Y. Tong, F. Wheeler and X. Liu, “Improving face recognition with a quality-based probabilistic framework”, in Proc. Computer Vision and Pattern Recognition (CVPR) conference 2009.
  • W. J. Scheirer, A. Bendale and T. E. Boult, “Predicting biometric facial recognition failure with similarity surfaces and support vector machines”, in Proc. of IEEE Computer Society Workshop on Biometrics, 2008.
  • M. T. Chan, R. Brown and W. Turner, “Incorporating quality metrics in multimodal biometric fusion”, in Proc. of IEEE Computer Society Workshop on Biometrics, pp 67-79, 2006.
  • K. Nandakumar, Y. Chen, A. K. Jain and S. C. Dass, “Quality-based score level fusion in multibiometric systems”, In International Conference on Pattern Recognition, 2006.
  • P. Wang, Q. Ji and J. L. Wayman, “Modeling and predicting face recognition system performance based on analysis of similarity scores”, in Proc. of IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29(4):665–670, 2007.
  • E. Tabassi, “NIST Fingerprint image quality and relation to PIV”, NIST Image Group Technical report, 2005.
  • J. S. Doyle, “Quality Metrics for Biometrics”, M.Sc. Thesis, Department of Computer Science and Engineering, University of Notre Dame, USA, 2011.
  • E. Tabassi, C. L. Wilson and C. I. Watson, “Fingerprint Image Quality”, Technical Report NISTIR 7151, NIST, 2004.
  • P. Liao, H. Lin, P. Zeng, S. Bai, H. Ma, S. Ding, “Facial Image Quality Assessment Based on Support Vector Machines”, in Proc. of International Conference on Biomedical Engineering and Biotechnology (iCBEB), pp. 810-813, 2012.
  • K Seshadrinathan, T. N. Pappas, R. J. Safranek, J. Chen, Z. Wang, H. R. Sheikh, and A. C. Bovik, “Image quality assessment”, Essential Guide to Image Processing, Elsevier, 2009.
  • C. Sasivarnan, A. Jagan, J. Kaur, D. Jyoti, D. S. Rao. “Image Quality Assessment Techniques on Spatial Domain”, IJCST, 2(3):177-184, 2011.
  • E. O. Omidiora, S. O. Olabiyisi, J. A. Ojo, R. A. Ganiyu, and A. Abayomi- Alli, “Enhanced Face Verification and Image Quality Assessment Scheme Using Modified Optical Flow Technique”, in Proc. WCECS’14 International Conference on Signal Processing and Imaging Engineering (ICSPIE'14), San Francisco, USA, 22nd-24th October, 2014.
  • R. Wallace, M. McLaren, C. McCool and S. Marcel, “Inter-session Variability Modelling and Joint Factor Analysis for Face Authentication”, in Proc. International Joint Conference on Biometrics (IJCB), 11-13 October, Arlington, Virginia, USA, pp 1-8, 2011.
  • J. H. Friedman, “Multivariate adaptive regression splines”, Annals of Statistics, 19(1):1-141, 1991.
  • P.J. Nieto, F. S. Lasheras, F. J. Juez, and J. R. Fernández, “Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain)”, Journal of Hazard Matter, 195 (2011): 414–421, 2011.
  • G. Jekabsons, “ARESLab: Adaptive Regression Splines toolbox for Matlab/Octave”, http://www.cs.rtu.lv/jekabsons/ Available online at
  • P. Craven and G. Wahba, “Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation,” Numer Math, Vol. 31:317- 403, 1979.
  • X. H. Chen and C. Z. Li, “Image quality assessment model based on features and applications in face recognition”, in Proc. IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1-4, 2011.
  • E. O. Omidiora, S. O. Olabiyisi, J. A. Ojo, A. Abayomi-Alli, F. Izilein, and P. I. Ezomo, "MACE Correlation Filter Algorithm for Face Verification in Surveillance Scenario", IJCSE, USA, 13(1):6-15, 2013.
  • E. O. Omidiora, S. O. Olabiyisi, J. A. Ojo, A. Abayomi-Alli, O. Abayomi- Alli, K. B. Erameh, “Facial Image Verification and Quality Assessment System – FaceIVQA”, International Journal of Electrical and Computer Engineering (IJECE), Malaysia, Vol. 3(6), 863-874, 2013.
  • A. Bouzerdoum, A. Havstad and A. Beghdadi, “Image quality assessment using a neural network approach”, Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, pp 330-333, 2004.
  • S. Bharadwaj, H. Bhatt, M. Vatsa, R. Singh and A. Noore, “Quality assessment based denoising to improve face recognition performance”, Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 140-145, 2011.
There are 34 citations in total.

Details

Primary Language English
Journal Section Reviews
Authors

A. A. Abayomi-alli This is me

E. O. Omidiora This is me

S. O. Olabiyisi This is me

J. A. Ojo This is me

Publication Date February 27, 2015
Published in Issue Year 2015 Volume: 3 Issue: 1

Cite

APA Abayomi-alli, A. . A., Omidiora, E. . O., Olabiyisi, S. . O., Ojo, J. . A. (2015). Adaptive Regression Splines Models for Predicting Facial Image Verification and Quality Assessment Scores. Balkan Journal of Electrical and Computer Engineering, 3(1), 17-26.

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