Research Article
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Year 2024, Volume: 7 Issue: 3, 361 - 377, 31.12.2024

Mohammad Mehdi Pazouki , Önsen Toygar , Mahdi Hosseinzadeh

https://doi.org/10.35377/saucis...1495856

Abstract

Project Number

Not available.

References

  • A. K. Jain, A. A. Ross, and K. Nandakumar, “Introduction to Biometrics”, Springer Publishing Company, Incorporated, 2011.
  • R. Szeliski, “Computer Vision: Algorithms and Applications”, 1st. ed., Springer-Verlag, Berlin, Heidelberg, 2010.
  • M. O. Oloyede, G. P. Hancke, H. C. Myburgh, “A review on face recognition systems: recent approaches and challenges”, Multimed Tools Appl, 79, pp. 27891–27922, 2020. https://doi.org/10.1007/s11042-020-09261-2
  • M. K. Rusia, D. K. Singh, “A comprehensive survey on techniques to handle face identity threats: challenges and opportunities”, Multimed Tools Appl, 82, pp. 1669–1748, 2023. https://doi.org/10.1007/s11042-022-13248-6
  • J. Yang, D. Zhang, Y. Xu, and J. Y. Yang, “Recognize color face images using complex Eigenfaces”, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3832 LNCS, pp. 64–68, 2006, doi: 10.1007/11608288_9.
  • A. W. Yip and P. Sinha, “Contribution of color to face recognition”, Perception, vol. 31, no. 8, pp. 995–1003, 2002, doi: 10.1068/p3376.
  • Q. B. Sun, W. M. Huang, and J. K. Wu, “Face detection based on color and local symmetry information”, Proc. - 3rd IEEE Int. Conf. Autom. Face Gesture Recognition (FG1998), pp. 130–135, 1998, doi: 10.1109/AFGR.1998.670937.
  • D. H. Brainard and B. A. Wandell, “Asymmetric color matching: how color appearance depends on the illuminant”, J. Opt. Soc. Am. A, vol. 9, no. 9, p. 1433, 1992, doi: 10.1364/josaa.9.001433.
  • M. Pedersen and J. Y. Hardeberg, “Full-Reference Image Quality Metrics: Classification and Evaluation”, Foundations and Trends in Computer Graphics and Vision, vol. 7, no. 1, pp. 1–80, 2012, doi: 10.1561/0600000037.
  • L. Liu, B. Liu, H. Huang and A. C. Bovik, “No-reference image quality assessment based on spatial and spectral entropies”, Signal Process. Image Commun., vol. 29, no. 8, pp. 856–863, 2014, doi: 10.1016/j.image.2014.06.006.
  • I. Chingovska, A. Anjos, S. Marcel, "On the Effectiveness of Local Binary Patterns in Face Anti-spoofing"; IEEE BIOSIG, 2012, https://ieeexplore.ieee.org/document/6313548
  • Facial Images: Faces94, Computer Vision Science Research Projects website, Designed and maintained by Dr Libor Spacek on 13th June 2009, https://cmp.felk.cvut.cz/~spacelib/faces/faces94.html
  • Color FERET Database, National Institute of Standards and Technology (NIST) website, Designed by P. Jonathon Phillips, Created January 31, 2011, Updated December 3, 2019, https://www.nist.gov/itl/products-and-services/color-feret-database
  • N. Khediri, M. Ammar and M. Kherallah, “Comparison of Image Segmentation using Different Color Spaces”. 2021 IEEE 21st International Conference on Communication Technology (ICCT), pp. 1188-1192, 2021, doi: 10.1109/ICCT52962.2021.9658094.
  • S. Banerji, A. Verma, and C. Liu, “Novel color LBP descriptors for scene and image texture classification”, Proc. 2011 Int. Conf. Image Process. Comput. Vision, Pattern Recognition, IPCV 2011, vol. 2, pp. 537–543, 2011.
  • D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”. International Journal of Computer Vision, vol. 60, pp. 91–110, 2004, https://doi.org/10.1023/B:VISI.0000029664.99615.94
  • H. Bay, A. Ess, T. Tuytelaars, L.V. Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), vol. 110, no. 3, pp. 346--359, 2008.
  • E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF”, Proc. IEEE Int. Conf. Comput. Vis., pp. 2564–2571, 2011, doi: 10.1109/ICCV.2011.6126544.
  • S. Kakarla, P. Gangula, M. S. Rahul, C. S. C. Singh, and T. H. Sarma, "Smart Attendance Management System Based on Face Recognition Using CNN", 2020 IEEE-HYDCON, Hyderabad, India, 2020, pp. 1-5, doi: 10.1109/HYDCON48903.2020.9242847.
  • A. Afaneh, F. Noroozi and Ö. Toygar, “Recognition of Identical Twins Using Fusion of Various Facial Feature Extractors”, EURASIP Journal on Image and Video Processing, vol. 2017:81, pp.1-14, Dec. 2017.
  • J. Galbally, S. Marcel, and J. Fierrez, "Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint and Face Recognition", IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 710.724, Feb. 2014, doi: 10.1109/TIP.2013.2292332.
  • Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, "A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects," in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 6999-7019, Dec. 2022, doi: 10.1109/TNNLS.2021.3084827.
  • T.K. Ho, “Nearest Neighbors in Random Subspaces”, In: A. Amin, D. Dori, P. Pudil, H. Freeman (eds), Lecture Notes in Computer Science, Springer, Germany, pp 640–648, 1998.
  • Z. Yu et al., “Searching central difference convolutional networks for face anti-spoofing”, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 5294–5304, 2020, doi: 10.1109/CVPR42600.2020.00534.
  • A. Benlamoudi et al., “Face Presentation Attack Detection Using Deep Background Subtraction”, Sensors, vol. 22, no. 10, 2022, doi: 10.3390/s22103760.
  • S. Karanwal and M. Diwakar, “Two novel color local descriptors for face recognition”, Optik (Stuttg)., vol. 226, 2021, doi: 10.1016/j.ijleo.2020.166007.
  • P. Terhörst, M. Huber, N. Damer, F. Kirchbuchner, and A. Kuijper, “Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition”, arXiv:2002.09181v1 [cs.CV], 2020, [Online]. Available: http://arxiv.org/abs/2002.09181.
  • B. Zhang, B. Tondi, and M. Barni, “Adversarial examples for replay attacks against CNN-based face recognition with anti-spoofing capability”, Comput. Vis. Image Underst., vol. 197–198, 2020, doi: 10.1016/j.cviu.2020.102988.
  • Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, "OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations", 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA, 2017, pp. 612-618, doi: 10.1109/FG.2017.77.
  • A. Vinay, A. Gupta, A. Bharadwaj, A. Srinivasan, K. N. B. Murthy, and S. Natarajan, “Deep Learning on Binary Patterns for Face Recognition”, Procedia Comput. Sci., vol. 132, pp. 76–83, 2018, doi: 10.1016/j.procs.2018.05.164.
  • L. F. Chen, H. Y. M. Liao, M. T. Ko, J. C. Lin, and G. J. Yu, “A New LDA-based Face Recognition System Which Can Solve the Small Sample Size Problem”, Proc. Jt. Conf. Inf. Sci., vol. 4, pp. 282–286, 1998.
  • J. Sikder, R. Chakma, R. J. Chakma, and U. K. Das, “Intelligent Face Detection and Recognition System”, 2021 Int. Conf. Intell. Technol. CONIT 2021, 2021, doi: 10.1109/CONIT51480.2021.9498291.
  • S. Karanwal, “A comparative study of 14 state of art descriptors for face recognition”, Multimed. Tools Appl., vol. 80, no. 8, pp. 12195–12234, 2021, doi: 10.1007/s11042-020-09833-2.
  • C.-K. Tran et al., “Local intensity area descriptor for facial recognition in ideal and noise conditions”, J. Electron. Imaging, vol. 26, no. 2, p. 023011, 2017, doi: 10.1117/1.jei.26.2.023011.
  • H. R. Chou, J. H. Lee, Y. M. Chan, and C. S. Chen, “Data-Specific Adaptive Threshold for Face Recognition and Authentication”, Proc. - 2nd Int. Conf. Multimed. Inf. Process. Retrieval, MIPR 2019, pp. 153–156, 2019, doi: 10.1109/MIPR.2019.00034.
  • W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J. Weng, “Discriminant Analysis of Principal Components for Face Recognition”, In: Wechsler, H., Phillips, P.J., Bruce, V., Soulié, F.F., Huang, T.S. (eds) Face Recognition. NATO ASI Series, vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72201-1_4
  • C. Geng and X. Jiang, “Face recognition using SIFT features”, Proc. - Int. Conf. Image Process. ICIP, pp. 3313–3316, 2009, doi: 10.1109/ICIP.2009.5413956.
  • G. Du, F. Su, and A. Cai, “Face recognition using SURF features”, MIPPR 2009 Pattern Recognit. Comput. Vis., vol. 7496, p. 749628, 2009, doi: 10.1117/12.832636.

A Hybrid Approach for Color Face Recognition Based on Image Quality Using Multiple Color Spaces

Year 2024, Volume: 7 Issue: 3, 361 - 377, 31.12.2024

Mohammad Mehdi Pazouki , Önsen Toygar , Mahdi Hosseinzadeh

https://doi.org/10.35377/saucis...1495856

Abstract

In this paper, the color face recognition problem is investigated using image quality assessment techniques and multiple color spaces. Image quality is measured using No-Reference Image Quality Assessment (NRIQA) techniques. Color face images are categorized into low, medium, and high-quality face images through the High Low Frequency Index (HLFI) measure. Based on the categorized face images, three feature extraction and classification methods as Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Convolutional Neural Networks (CNN) are applied to face images using RGB, YCbCr, and HSV color spaces to extract the features and then classify the images for face recognition. To enhance color face recognition systems' robustness, a hybrid approach that integrates the aforementioned methods is proposed. Additionally, the proposed system is designed to serve as a secure anti-spoofing mechanism, tested against different attack scenarios, including print attacks, mobile attacks, and high-definition attacks. A comparative analysis that assesses the proposed approach with the state-of-the-art systems using Faces94, ColorFERET, and Replay Attack datasets is presented. The proposed method achieves 96.26%, 100%, and 100% accuracies on ColorFERET, Replay Attack, and Faces94 datasets, respectively. The results of this analysis show that the proposed method outperforms existing methods. The proposed method showcases the potential for more reliable and secure recognition systems.

Keywords

Face recognition Image quality assessment measures Color spaces Feature extraction Deep learning

Ethical Statement

Not available.

Supporting Institution

Not available.

Project Number

Not available.

Thanks

The authors would like to thank Idiap Research Institute in Switzerland for providing Replay Attack database; Dr. Libor Spacek for providing Faces94 database; Dr. P. Jonathon Phillips from National Instutute of Standards and Technology for providing ColorFERET database.

References

  • A. K. Jain, A. A. Ross, and K. Nandakumar, “Introduction to Biometrics”, Springer Publishing Company, Incorporated, 2011.
  • R. Szeliski, “Computer Vision: Algorithms and Applications”, 1st. ed., Springer-Verlag, Berlin, Heidelberg, 2010.
  • M. O. Oloyede, G. P. Hancke, H. C. Myburgh, “A review on face recognition systems: recent approaches and challenges”, Multimed Tools Appl, 79, pp. 27891–27922, 2020. https://doi.org/10.1007/s11042-020-09261-2
  • M. K. Rusia, D. K. Singh, “A comprehensive survey on techniques to handle face identity threats: challenges and opportunities”, Multimed Tools Appl, 82, pp. 1669–1748, 2023. https://doi.org/10.1007/s11042-022-13248-6
  • J. Yang, D. Zhang, Y. Xu, and J. Y. Yang, “Recognize color face images using complex Eigenfaces”, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3832 LNCS, pp. 64–68, 2006, doi: 10.1007/11608288_9.
  • A. W. Yip and P. Sinha, “Contribution of color to face recognition”, Perception, vol. 31, no. 8, pp. 995–1003, 2002, doi: 10.1068/p3376.
  • Q. B. Sun, W. M. Huang, and J. K. Wu, “Face detection based on color and local symmetry information”, Proc. - 3rd IEEE Int. Conf. Autom. Face Gesture Recognition (FG1998), pp. 130–135, 1998, doi: 10.1109/AFGR.1998.670937.
  • D. H. Brainard and B. A. Wandell, “Asymmetric color matching: how color appearance depends on the illuminant”, J. Opt. Soc. Am. A, vol. 9, no. 9, p. 1433, 1992, doi: 10.1364/josaa.9.001433.
  • M. Pedersen and J. Y. Hardeberg, “Full-Reference Image Quality Metrics: Classification and Evaluation”, Foundations and Trends in Computer Graphics and Vision, vol. 7, no. 1, pp. 1–80, 2012, doi: 10.1561/0600000037.
  • L. Liu, B. Liu, H. Huang and A. C. Bovik, “No-reference image quality assessment based on spatial and spectral entropies”, Signal Process. Image Commun., vol. 29, no. 8, pp. 856–863, 2014, doi: 10.1016/j.image.2014.06.006.
  • I. Chingovska, A. Anjos, S. Marcel, "On the Effectiveness of Local Binary Patterns in Face Anti-spoofing"; IEEE BIOSIG, 2012, https://ieeexplore.ieee.org/document/6313548
  • Facial Images: Faces94, Computer Vision Science Research Projects website, Designed and maintained by Dr Libor Spacek on 13th June 2009, https://cmp.felk.cvut.cz/~spacelib/faces/faces94.html
  • Color FERET Database, National Institute of Standards and Technology (NIST) website, Designed by P. Jonathon Phillips, Created January 31, 2011, Updated December 3, 2019, https://www.nist.gov/itl/products-and-services/color-feret-database
  • N. Khediri, M. Ammar and M. Kherallah, “Comparison of Image Segmentation using Different Color Spaces”. 2021 IEEE 21st International Conference on Communication Technology (ICCT), pp. 1188-1192, 2021, doi: 10.1109/ICCT52962.2021.9658094.
  • S. Banerji, A. Verma, and C. Liu, “Novel color LBP descriptors for scene and image texture classification”, Proc. 2011 Int. Conf. Image Process. Comput. Vision, Pattern Recognition, IPCV 2011, vol. 2, pp. 537–543, 2011.
  • D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”. International Journal of Computer Vision, vol. 60, pp. 91–110, 2004, https://doi.org/10.1023/B:VISI.0000029664.99615.94
  • H. Bay, A. Ess, T. Tuytelaars, L.V. Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), vol. 110, no. 3, pp. 346--359, 2008.
  • E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF”, Proc. IEEE Int. Conf. Comput. Vis., pp. 2564–2571, 2011, doi: 10.1109/ICCV.2011.6126544.
  • S. Kakarla, P. Gangula, M. S. Rahul, C. S. C. Singh, and T. H. Sarma, "Smart Attendance Management System Based on Face Recognition Using CNN", 2020 IEEE-HYDCON, Hyderabad, India, 2020, pp. 1-5, doi: 10.1109/HYDCON48903.2020.9242847.
  • A. Afaneh, F. Noroozi and Ö. Toygar, “Recognition of Identical Twins Using Fusion of Various Facial Feature Extractors”, EURASIP Journal on Image and Video Processing, vol. 2017:81, pp.1-14, Dec. 2017.
  • J. Galbally, S. Marcel, and J. Fierrez, "Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint and Face Recognition", IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 710.724, Feb. 2014, doi: 10.1109/TIP.2013.2292332.
  • Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, "A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects," in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 6999-7019, Dec. 2022, doi: 10.1109/TNNLS.2021.3084827.
  • T.K. Ho, “Nearest Neighbors in Random Subspaces”, In: A. Amin, D. Dori, P. Pudil, H. Freeman (eds), Lecture Notes in Computer Science, Springer, Germany, pp 640–648, 1998.
  • Z. Yu et al., “Searching central difference convolutional networks for face anti-spoofing”, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 5294–5304, 2020, doi: 10.1109/CVPR42600.2020.00534.
  • A. Benlamoudi et al., “Face Presentation Attack Detection Using Deep Background Subtraction”, Sensors, vol. 22, no. 10, 2022, doi: 10.3390/s22103760.
  • S. Karanwal and M. Diwakar, “Two novel color local descriptors for face recognition”, Optik (Stuttg)., vol. 226, 2021, doi: 10.1016/j.ijleo.2020.166007.
  • P. Terhörst, M. Huber, N. Damer, F. Kirchbuchner, and A. Kuijper, “Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition”, arXiv:2002.09181v1 [cs.CV], 2020, [Online]. Available: http://arxiv.org/abs/2002.09181.
  • B. Zhang, B. Tondi, and M. Barni, “Adversarial examples for replay attacks against CNN-based face recognition with anti-spoofing capability”, Comput. Vis. Image Underst., vol. 197–198, 2020, doi: 10.1016/j.cviu.2020.102988.
  • Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, "OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations", 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA, 2017, pp. 612-618, doi: 10.1109/FG.2017.77.
  • A. Vinay, A. Gupta, A. Bharadwaj, A. Srinivasan, K. N. B. Murthy, and S. Natarajan, “Deep Learning on Binary Patterns for Face Recognition”, Procedia Comput. Sci., vol. 132, pp. 76–83, 2018, doi: 10.1016/j.procs.2018.05.164.
  • L. F. Chen, H. Y. M. Liao, M. T. Ko, J. C. Lin, and G. J. Yu, “A New LDA-based Face Recognition System Which Can Solve the Small Sample Size Problem”, Proc. Jt. Conf. Inf. Sci., vol. 4, pp. 282–286, 1998.
  • J. Sikder, R. Chakma, R. J. Chakma, and U. K. Das, “Intelligent Face Detection and Recognition System”, 2021 Int. Conf. Intell. Technol. CONIT 2021, 2021, doi: 10.1109/CONIT51480.2021.9498291.
  • S. Karanwal, “A comparative study of 14 state of art descriptors for face recognition”, Multimed. Tools Appl., vol. 80, no. 8, pp. 12195–12234, 2021, doi: 10.1007/s11042-020-09833-2.
  • C.-K. Tran et al., “Local intensity area descriptor for facial recognition in ideal and noise conditions”, J. Electron. Imaging, vol. 26, no. 2, p. 023011, 2017, doi: 10.1117/1.jei.26.2.023011.
  • H. R. Chou, J. H. Lee, Y. M. Chan, and C. S. Chen, “Data-Specific Adaptive Threshold for Face Recognition and Authentication”, Proc. - 2nd Int. Conf. Multimed. Inf. Process. Retrieval, MIPR 2019, pp. 153–156, 2019, doi: 10.1109/MIPR.2019.00034.
  • W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J. Weng, “Discriminant Analysis of Principal Components for Face Recognition”, In: Wechsler, H., Phillips, P.J., Bruce, V., Soulié, F.F., Huang, T.S. (eds) Face Recognition. NATO ASI Series, vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72201-1_4
  • C. Geng and X. Jiang, “Face recognition using SIFT features”, Proc. - Int. Conf. Image Process. ICIP, pp. 3313–3316, 2009, doi: 10.1109/ICIP.2009.5413956.
  • G. Du, F. Su, and A. Cai, “Face recognition using SURF features”, MIPPR 2009 Pattern Recognit. Comput. Vis., vol. 7496, p. 749628, 2009, doi: 10.1117/12.832636.
There are 38 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Mohammad Mehdi Pazouki 0000-0002-8427-250X

Önsen Toygar 0000-0001-7402-9058

Mahdi Hosseinzadeh 0000-0002-3255-3473

Project Number Not available.
Early Pub Date November 29, 2024
Publication Date December 31, 2024
Submission Date June 4, 2024
Acceptance Date September 18, 2024
Published in Issue Year 2024Volume: 7 Issue: 3

Cite

IEEE M. M. Pazouki, Ö. Toygar, and M. Hosseinzadeh, “A Hybrid Approach for Color Face Recognition Based on Image Quality Using Multiple Color Spaces”, SAUCIS, vol. 7, no. 3, pp. 361–377, 2024, doi: 10.35377/saucis...1495856.
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