Skip to main content
SpringerLink
Log in

Cross domain association using transfer subspace learning

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Transfer learning has gained more attention recently by utilizing knowledge acquired from one domain to advance a learning performance in another domain. Existing homogeneous transfer learning methods have progressed to a point where feature spaces are common in training and testing domains. However, heterogeneous transfer learning is still in its nascent stage where features of training and testing domains are different. Taking this into account, Bregman Divergence Regularization is used to minimize the probability distribution difference between training and testing domains and to take them together to a shared subspace. To discriminate data within individual domains, a projection matrix is obtained using Fisher Linear Discriminant Analysis subspace learning algorithm. Experimentation is performed on two efficiently used biometrics: the face and fingerprint. Two types of cross-domain settings are used: (1) Face + Finger2Finger where training samples come from face (labeled samples) and fingerprint (unlabeled samples) data sets, while testing is performed on a fingerprint dataset. (2) Finger + Face2Face where training samples come from fingerprint (labeled samples) and face (unlabeled samples) data sets while testing is performed on a face dataset. This paper proposes a cross domain association between face and fingerprint that finds utility in forensic applications.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Obozinski G, Taskar B, Jordan MI (2006) Multi-task feature selection, technical report, Dept. of Statistics, Uni. of California, Berkeley

  2. Blitzer J, McDonald R, Pereira F (2006) Domain adaptation with structural correspondence learning. In: Proc. of the Conf. on Empirical Methods in Natural Language, pp 120–128

  3. Blitze J, Crammer K, Kulesza A, Pereira F et al (2008) Learning bounds for domain adaptation. Adv Neural Inf Process Syst 21:129–136

    Google Scholar 

  4. Pan SJ, Kwok JT, Yang Q (2008) Transfer Learning via dimensionality reduction. In: Proc. 23rd National Conf. Artificial Intelligence pp. 677–682

  5. Si S, Tao D, Geng B (2010) Bregman Divergence-based regularization for transfer subspace learning. IEEE Trans Knowl Data En 22(7):929–942

    Article  Google Scholar 

  6. Belkin M, Niyogi P, Sindhwani V, Regularization M (2006) A geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434

    MathSciNet  MATH  Google Scholar 

  7. Cai D, He X, Han J (2007) Semi-supervised discriminant analysis. In: IEEE Int Conf. Computer Vision, pp 1–7

  8. Zhang T, Tao D, Yang J (2008) Discriminative locality alignment. In: Proc. 10th European Conf. computer vision, pp 725–738

  9. Caruana R (1997) Multitask learning. Mach Learn 28(1):41–75

    Article  MathSciNet  Google Scholar 

  10. Whitbrook AM, Aickelin U, Garibaldi JM (2010) Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms. Evol Intel 3:123–136

    Article  Google Scholar 

  11. Zadrozny B (2004) Learning and evaluating classifiers under sample selection bias. In: International Conf. Machine Learning, pp 114–121

  12. Smith RE, Jiang MK, Bacardit J et al (2010) A learning classifier system with mutual-information-based fitness. Evol Intel 3(1):31–50

    Article  Google Scholar 

  13. Do CB, Ng AY (2006) Transfer learning for text classification. Adv Neural Inf Process Syst 2006:299–306

    Google Scholar 

  14. Farhadi A, Forsyth D, White R (2007) Transfer learning in sign language. In: Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp 1–8

  15. Gong B, Grauman K, Sha F (2014) Learning kernels for unsupervised domain adaptation with applications to visual object recognition. Int J Comput Vis 109:3–27

    Article  MathSciNet  MATH  Google Scholar 

  16. Nguyen TT, Silander T, Li Z et al (2017) Scalable transfer learning in heterogeneous, dynamic environments. Artif Intell 247:70–94

    Article  MathSciNet  MATH  Google Scholar 

  17. Zhao L, Pan SJ, Yang Q (2017) A unified framework of active transfer learning for cross-system recommendation. Artif Intell 245:38–55

    Article  MathSciNet  MATH  Google Scholar 

  18. Pan W, Yang Q (2013) Transfer learning in heterogeneous collaborative filtering domains. Artif Intell 197:39–55

    Article  MathSciNet  MATH  Google Scholar 

  19. Zhao P, Hoi SCH, Wang J, Li B (2014) Online transfer learning. Artif Intell 216:76–102

    Article  MathSciNet  MATH  Google Scholar 

  20. Kute RS, Vyas V (2016) Biometric association using transfer subspace learning. In: IEEE Region 10 Conf. (TENCON), pp 1384–1387

  21. Kute RS, Vyas V, Anuse A (2019) Component-based face recognition under transfer learning for forensic applications. Inf Sci 476:176–191

    Article  Google Scholar 

  22. Lim D, Ong YS, Gupta A et al (2016) Towards a new praxis in optinformatics targeting knowledge re-use in evolutionary computation: simultaneous problem learning and optimization. Evol Intel 9(4):203–220

    Article  Google Scholar 

  23. Xia S, Shao M (2012) Jiebo, Understanding kin relationship in a photo. IEEE Trans Multimedia 14(4):1046–1056

    Article  Google Scholar 

  24. Su Y, Fu Y, Tian Q, Gao X (2010) Cross-database age estimation based on transfer learning. In: Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp 1270–1273

  25. Yan H, Marcelo H, Poo A (2011) Cross-dataset facial expression recognition. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp 5985–5990

  26. Si S, Tao D, Chan (2010) Evolutionary cross-domain discriminative hessian eigenmaps. IEEE Trans Image Process 19(4):1075–1086

    Article  MathSciNet  MATH  Google Scholar 

  27. Si S, Tao D, Wang M et al (2010) Social image annotation via crossdomain subspace learning. Springer Publ Multimedia Tools Appl 56:91–108

    Article  Google Scholar 

  28. Galea C, Farrugia A (2017) Matching software-generated sketches to face photos with a very deep CNN, morphed faces, and transfer learning. IEEE Trans Inf Forensics Secur 99:1556–6013

    Google Scholar 

  29. Kang S (2018) On effectiveness of transfer learning approach for neural network-based virtual metrology modeling. IEEE Trans Semicond Manuf 31(1):149–155

    Article  Google Scholar 

  30. Masood S, Luthra T, Sundriyal H et al (2017) Identification of diabetic retinopathy in eye images using transfer learning. Int Conf Comput Commun Autom 2017:1183–1187

    Google Scholar 

  31. Chang J, Yu J, Han T et al (2017) A method for classifying medical images using transfer learning: a pilot study on histopathology of breast cancer. In: IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), pp 1–4

  32. Hon M, Khan NM (2017) Towards Alzheimer’s disease classification through transfer learning. IEEE Int Conf Bioinf Biomed 2017:1166–1169

    Google Scholar 

  33. Wang J, Wang G, Zhou M (2018) Bimodal vein data mining via cross-selected domain knowledge transfer. IEEE Trans Inf Forensics Secur 13(3):733–744

    Article  Google Scholar 

  34. Sentas A, Tashiev I, Kucukayvaz F et al (2018) Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification. Evol Intel 2018:1–9

    Google Scholar 

  35. Anuse A, Vyas V (2016) A novel training algorithm for convolutional neural network. Complex Intell Syst 2(3):221–234

    Article  Google Scholar 

  36. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  37. He X, Cai D, Han J (2008) Learning a maximum margin subspace for image retrieval. IEEE Trans Knowl Data Eng 20(2):189–201

    Article  Google Scholar 

  38. Neerja E, Walia (2008) Face recognition using improved fast CA algorithm. Congr Image Sign Process 1:554–558

    Article  Google Scholar 

  39. Zhujie Y, Yu l (1994) Face recognition with eigenfaces. In: IEEE Int. Conf. on Industrial Technology, pp 434–438

  40. Sharkas M, AbouElenien M (2008) Eigenfaces vs. fisherfaces vs. ICA for face recognition: a comparative study, 9th Int. Conf. on Signal Processing, pp 914–919

  41. Huang P, Caikou C (2009) Enhanced marginal fisher analysis for face recognition. Int Conf Artif Intell Comput Intell 2:403–407

    Google Scholar 

  42. Ashalatha ME et al (2014) Face recognition using local features by LPP approach. Int. Conf. on Circuits, Communication, Control and Computing, pp 382–386

  43. Zhang T et al (2008) Discriminative ocality alignment, lecture notes in computer science computer vision, pp 725–738

  44. Liu F, Zhang G, Lu J (2017) Heterogeneous transfer learning: an unsupervised approach. IEEE Int. Conf. on fuzzy systems (FUZZ-IEEE), pp 1–6

  45. Grm K, Struc V et al (2018) Strengths and weaknesses of deep learning models for face recognition against image degradations. IET Biometrics 7(1):81–89

    Article  Google Scholar 

  46. Sonkamble S, Thool DR (2010) Survey of biometric recognition systems and their applications. J Theor Appl Inf Technol 2010:11

    Google Scholar 

  47. Mahafzah Al, Rwashdeh Al (2012) A survey of multibiometric systems, arXiv preprint

  48. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7:179–188

    Article  Google Scholar 

  49. Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. IEEE Workshop on Applications of Computer Vision, pp 138–142

Download references

Author information

Authors and Affiliations

  1. Department of E&TC, College of Engineering, Shivajinagar, Pune, 411005, India

    Rupali Sandip Kute & Vibha Vyas

  2. Department of E&TC, Maharashtra Institute of Technology, Kothrud, Pune, 411038, India

    Rupali Sandip Kute & Alwin Anuse

Authors
  1. Rupali Sandip Kute
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vibha Vyas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alwin Anuse
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rupali Sandip Kute.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kute, R.S., Vyas, V. & Anuse, A. Cross domain association using transfer subspace learning. Evol. Intel. 12, 201–209 (2019). https://doi.org/10.1007/s12065-019-00211-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00211-y

Keywords

Search

Navigation