Skip to main content
SpringerLink
Log in

Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets

  • Chapter
  • First Online:
Book cover Deep Biometrics

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

There are still problems in applications of Machine Learning for face recognition. Such factors as lighting conditions, head rotations, emotions, and view angles affect the recognition accuracy. A large number of recognition subjects requires complex class boundaries. Deep Neural Networks have provided efficient solutions, although their implementations require massive computations for evaluation and minimisation of error functions. Gradient algorithms provide iterative minimisation of the error function. A maximal performance is achieved if parameters of gradient algorithms and neural network structures are properly set. The use of pairwise neural network structures often improves the performance because such structures require a small set of optimisation parameters. The experiments have been conducted on some face biometric benchmark data sets, and the main findings are presented in the form of a tutorial.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. H. Bay, T. Tuytelaars, L.V. Gool, Surf: speeded up robust features, in Computer Vision – ECCV 2006, ed. by A. Leonardis, H. Bischof, A. Pinz (Springer, Berlin, 2006), pp. 404–417

    Chapter  Google Scholar 

  2. H. Bay, A. Ess, T. Tuytelaars, L.V. Gool, Speeded-up robust features (surf). Comp. Vision Image Underst. (CVIU) 110(3), 346–359 (2008)

    Article  Google Scholar 

  3. F. Bowen, J. Hu, E.Y. Du, A multistage approach for image registration. IEEE Trans. Cybern. 46(9), 2119–2131 (2016)

    Article  Google Scholar 

  4. B.C. Chen, Y.Y. Chen, Y.H. Kuo, T.D. Ngo, D.D. Le, S. Satoh, W.H. Hsu, Scalable face track retrieval in video archives using bag-of-faces sparse representation. IEEE Trans. Circuits Syst. Video Technol. 27(7), 1595–1603 (2017)

    Article  Google Scholar 

  5. Z. Chen, Y. Wang, H. Liu, Unobtrusive sensor-based occupancy facing direction detection and tracking using advanced machine learning algorithms. IEEE Sensors J. 18(15), 6360–6368 (2018)

    Article  Google Scholar 

  6. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1 (June 2005), pp. 886–893

    Google Scholar 

  7. W. Deng, J. Hu, J. Lu, J. Guo, Transform-invariant PCA: a unified approach to fully automatic face alignment, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1275–1284 (2014)

    Article  Google Scholar 

  8. L. Fei-Fei, R. Fergus, P. Perona, Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007)

    Article  Google Scholar 

  9. S. Frintrop, E. Rome, H.I. Christensen, Computational visual attention systems and their cognitive foundations: a survey. ACM Trans. Appl. Percept. 7(1), 1–39 (2010)

    Article  Google Scholar 

  10. A.C. Geng Du, F. Su, Face recognition using surf features, in Proceedings SPIE, vol. 7496 (2009)

    Google Scholar 

  11. A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  12. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, Cambridge, 2016)

    MATH  Google Scholar 

  13. S. Gunasekar, J. Ghosh, A.C. Bovik, Face detection on distorted images augmented by perceptual quality-aware features. IEEE Trans. Inf. Forensics Secur. 9(12), 2119–2131 (2014)

    Article  Google Scholar 

  14. F.M. Hasanuzzaman, X. Yang, Y. Tian, Robust and effective component-based banknote recognition by surf features, in 2011 20th Annual Wireless and Optical Communications Conference (WOCC) (April 2011), pp. 1–6

    Google Scholar 

  15. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning. Springer Series in Statistics (Springer, New York, 2001)

    Chapter  Google Scholar 

  16. A.J. Izenman, Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning,1st edn. (Springer, Berlin, 2008)

    Book  Google Scholar 

  17. L. Jakaite, V. Schetinin, C. Maple, J. Schult, Bayesian decision trees for EEG assessment of newborn brain maturity, in 2010 UK Workshop on Computational Intelligence (UKCI) (Sept 2010), pp. 1–6

    Google Scholar 

  18. L. Jakaite, V. Schetinin, J. Schult, Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity, in 2011 24th International Symposium on Computer-Based Medical Systems (CBMS) (June 2011), pp. 1–6

    Google Scholar 

  19. B. Jun, I. Choi, D. Kim, Local transform features and hybridization for accurate face and human detection. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1423–1436 (2013)

    Article  Google Scholar 

  20. T. Kalsum, S.M. Anwar, M. Majid, B. Khan, S.M. Ali, Emotion recognition from facial expressions using hybrid feature descriptors. IET Image Process. 12(6), 1004–1012 (2018)

    Article  Google Scholar 

  21. V. Kecman, T.-M. Huang, M. Vogt, Iterative single data algorithm for training kernel machines from huge data sets: theory and performance. Support Vector Mach. Theory Appl. 177, 605–605 (2005)

    Google Scholar 

  22. Y. LeCun, Y. Bengio, G.E. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  23. Y. Lei, X. Jiang, Z. Shi, D. Chen, Q. Li, Face recognition method based on surf feature, in 2009 International Symposium on Computer Network and Multimedia Technology (Jan 2009), pp. 1–4

    Google Scholar 

  24. J. Li, T. Wang, Y. Zhang, Face detection using surf cascade, in 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) (Nov 2011), pp. 2183–2190

    Google Scholar 

  25. Z. Li, U. Park, A.K. Jain, A discriminative model for age invariant face recognition. IEEE Trans. Inf. Forensics Secur. 6(3), 1028–1037 (2011)

    Article  Google Scholar 

  26. H. Li, Z. Lin, X. Shen, J. Brandt, G. Hua, A convolutional neural network cascade for face detection, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 00 (June 2015), pp. 5325–5334

    Google Scholar 

  27. L. Liu, C. Xiong, H. Zhang, Z. Niu, M. Wang, S. Yan, Deep aging face verification with large gaps. IEEE Trans. Multimedia 18(1), 64–75 (2016)

    Article  Google Scholar 

  28. D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  29. M.J. Lyons, S. Akamatsu, M. Kamachi, J. Gyoba, Coding facial expressions with Gabor wavelets, in FG (1998)

    Google Scholar 

  30. W. Mei, D. Weihong, Deep face recognition: a survey. CoRR, abs/1804.06655 (2018)

    Google Scholar 

  31. K.P. Murphy, Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, 2012)

    MATH  Google Scholar 

  32. A. Nefian, M. Khosravi, M. Hayes, Real-time detection of human faces in uncontrolled environments, in Proceedings of SPIE - The International Society for Optical Engineering (May 1999)

    Google Scholar 

  33. N. Nyah, L. Jakaite, V. Schetinin, P. Sant, A. Aggoun, Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data, in 2016 SAI Computing Conference (SAI) (July 2016), pp. 409–413

    Google Scholar 

  34. S. O’Hara, B.A. Draper, Introduction to the bag of features paradigm for image classification and retrieval. arXiv, abs/1101.3354 (2011)

    Google Scholar 

  35. M. Oravec, Feature extraction and classification by machine learning methods for biometric recognition of face and iris, in Proceedings ELMAR-2014 (Sept 2014), pp. 1–4

    Google Scholar 

  36. E. Oyallon, J. Rabin, An analysis of the SURF method. Image Processing On Line 5, 176–218 (2015)

    Article  MathSciNet  Google Scholar 

  37. N. Passalis, A. Tefas, Learning neural bag-of-features for large-scale image retrieval. IEEE Trans. Syst. Man Cybern. Syst. 47(10), 2641–2652 (2017)

    Article  Google Scholar 

  38. D.E. Rumelhart, G.E. Hinton, R.J. Wilson, Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  39. F. Samaria, A. Harter, Parameterisation of a stochastic model for human face identification, in Winter Conference On Applications of Computer Vision (1994)

    Google Scholar 

  40. E. Sangineto, Pose and expression independent facial landmark localization using dense-SURF and the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 624–638 (2013)

    Article  Google Scholar 

  41. U. Scherhag, R. Raghavendra, K.B. Raja, M. Gomez-Barrero, C. Rathgeb, C. Busch, On the vulnerability of face recognition systems towards morphed face attacks, in 2017 5th International Workshop on Biometrics and Forensics (IWBF) (April 2017), pp. 1–6

    Google Scholar 

  42. V. Schetinin, L. Jakaite, Extraction of features from sleep EEG for Bayesian assessment of brain development. PLOS One 12(3), 1–13 (2017)

    Article  Google Scholar 

  43. V. Schetinin, L. Jakaite, W.J. Krzanowski, Prediction of survival probabilities with Bayesian decision trees. Expert Syst. Appl. 40(14), 5466–5476 (2013)

    Article  Google Scholar 

  44. V. Schetinin, L. Jakaite, N. Nyah, D. Novakovic, W. Krzanowski, Feature extraction with GMDH-Type neural networks for EEG-based person identification. Int. J. Neural Syst. 2018, 1750064 (2017)

    Google Scholar 

  45. V. Schetinin, L. Jakaite, N. Nyah, D. Novakovic, W. Krzanowski, Feature extraction with GMDH-type neural networks for EEG-based person identification. Int. J. Neural Syst. 28, 1750064 (2018)

    Article  Google Scholar 

  46. C. Shu, X. Ding, C. Fang, Histogram of the oriented gradient for face recognition. Tsinghua Sci. Technol. 16(2), 216–224 (2011)

    Article  Google Scholar 

  47. L. Spacek, Face Recognition Data (June 2008), Online. Accessed 13 Dec 2018

    Google Scholar 

  48. H.H. Su, T.W. Chen, C.C. Kao, W.H. Hsu, S.Y. Chien, Preference-aware view recommendation system for scenic photos based on bag-of-aesthetics-preserving features. IEEE Trans. Multimedia 14(3), 833–843 (2012)

    Article  Google Scholar 

  49. Support vector machine template - MATLAB templateSVM (Dec 2018). Accessed 13 Dec 2018

    Google Scholar 

  50. H. Tan, B. Yang, Z. Ma, Face recognition based on the fusion of global and local HOG features of face images. IET Comput. Vis. 8(3), 224–234 (2014)

    Article  Google Scholar 

  51. J. Uglov, L. Jakaite, V. Schetinin, C. Maple, Comparing robustness of pairwise and multiclass neural-network systems for face recognition. EURASIP J. Adv. Signal Process. 2008, Article ID 468693, 7 pp. (2008). https://doi.org/10.1155/2008/468693

  52. Z. Wu, Q. Ke, J. Sun, H.Y. Shum, Scalable face image retrieval with identity-based quantization and multireference reranking. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1991–2001 (2011)

    Article  Google Scholar 

  53. Z. Xiang, H. Tan, W. Ye, The excellent properties of a dense grid-based HOG feature on face recognition compared to Gabor and LBP. IEEE Access 6, 29,306–29,319 (2018)

    Article  Google Scholar 

  54. Z. Xie, P. Jiang, S. Zhang, Fusion of LBP and hog using multiple kernel learning for infrared face recognition, in 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) (May 2017), pp. 81–84

    Google Scholar 

  55. C. Xu, Y. Wang, T. Tan, L. Quan, Automatic 3d face recognition combining global geometric features with local shape variation information, in Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings (May 2004), pp. 308–313

    Google Scholar 

  56. G. Zhang, Y. Wang, Robust 3d face recognition based on resolution invariant features. Pattern Recogn. Lett. 32(7), 1009–1019 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Bedfordshire, School of Computer Science and Technology, Luton, UK

    Natalya Selitskaya, S. Sielicki, L. Jakaite, V. Schetinin, F. Evans, M. Conrad & P. Sant

Authors
  1. Natalya Selitskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Sielicki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Jakaite
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. Schetinin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. F. Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Conrad
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. P. Sant
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computing and Communication, InfoLab21, Lancaster University, Lancaster, UK

    Richard Jiang

  2. School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia

    Chang-Tsun Li

  3. Electrical Engineering and Computer Science, School of Electronics, Queen’s University Belfast, Belfast, UK

    Danny Crookes

  4. Technical University of Denmark, Kgs. Lyngby, Denmark

    Weizhi Meng

  5. GREYC research Lab, ENSICAEN, Caen, France

    Christophe Rosenberger

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Selitskaya, N. et al. (2020). Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets. In: Jiang, R., Li, CT., Crookes, D., Meng, W., Rosenberger, C. (eds) Deep Biometrics. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-32583-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32583-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32582-4

  • Online ISBN: 978-3-030-32583-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation