Skip to main content

Advertisement

Springer Nature Link
Log in

VISA: a multimodal database of face and iris traits

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, a new realistic and challenging Face-Iris multimodal biometric database called VISA database is described. One significant problem associated with the development and evaluation of multimodal biometric systems using face and iris biometric traits is the lack of publicly available multimodal databases that are acquired in an unconstrained environment. Currently, there exist no multimodal databases containing a sufficient number of common subjects involved in both face and iris data acquisition process under different conditions. The VISA database fulfills these requirements and it will be a useful tool for the design and development of new algorithms for developing multimodal biometric systems. The VISA iris images are acquired using the IriShield camera. Face images are captured using mobile device. The corpus of a new VISA database consists of face images that vary in expression, pose and illumination, and presence of occlusion whereas iris images vary in illumination, eye movement, and occlusion. A total of more than 5000 images of 100 subjects are collated and used to form the new database. The key features of the VISA dataset are the wide and diverse population of subjects (age and gender). The VISA database is able to support face and/or iris unimodal or multimodal biometric recognition. Hence, the VISA database is a useful addition for the purpose of research and development of biometric systems based on face and iris biometrics. This paper also describes the baseline results of state-of-the-art methods on the VISA dataset and other popular similar datasets. The VISA database will be made available to the public through https://vtu.ac.in/en/visa-multimodal-face-and-iris-biometrics-database/

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

References

  1. Angadi SA, Kagawade VC (2017) A robust face recognition approach through symbolic modeling of polar FFT features. Pattern Recogn 71C(2017):235–248. https://doi.org/10.1016/j.patcog.2017.06.014

    Article  Google Scholar 

  2. Angadi SA, Kagawade VC (2017) Iris Recognition using Savitzky-Golay Filter Energy Feature through Symbolic Data Modeling. 6th IEEE international conference on Advances in Computing, Communications and Informatics(ICACCI 2017),13-16 September 2017, Manipal University, Karanataka, ISBN: 978-1-5090-6367-3,Jan. 06, 2017

  3. Angadi SA, Kagawade VC (2017) Iris Recognition: A Symbolic Data Modeling Approach using Savitzky-Golay Filter Energy Features. International conference on Smart Technologies for Smart Nation, REVA University, Bangalore, ISBN: 978–1–5386-0568-4, 17–19 August 2017

  4. Angadi SA, Kagawade VC (2018) Face and Iris wavelet feature fusion through canonical correlation analysis for person identification. 2018 international conference on electrical, electronics, communication, computer, and optimization techniques (ICEECCOT), Msyuru, India, 2018, pp. 172–178. doi: https://doi.org/10.1109/ICEECCOT43722.2018.9001636

  5. Benalcazar DP, Zambrano JE, Bastias D, Perez CA, Bowyer KW (2020) A 3D Iris scanner from a single image using convolutional neural networks. IEEE Access 8:98584–98599. https://doi.org/10.1109/ACCESS.2020.2996563

    Article  Google Scholar 

  6. Bianco S (2017) Large Age-Gap face verification by feature injection in deep networks. Pattern Recognition Letters, Volume 90,2017, Pages 36–42, ISSN 0167-8655. https://doi.org/10.1016/j.patrec.2017.03.006

  7. Chen Y, Wu C, Wang Y (2020) T-Center: A Novel Feature Extraction Approach Towards Large-Scale Iris Recognition. IEEE Access, vol. 8, pp. 32365-32375, 2020. https://doi.org/10.1109/ACCESS.2020.2973433

  8. Daugman J (2004) How iris recognition works. Circuits and Systems for. Video Technology, IEEE Transactions on, vol. 14, no. 1, pp. 21–30, 2004

  9. Dessimoz D, Richiardi J, Champod C, Drygajlo A (2007) Multimodal biometrics for identity documents (MBioID). Forensic Science International. 2007 Apr;167(2-3):154–159.  https://doi.org/10.1016/j.forsciint.2006.06.037

  10. Dumas B, Pugin C, Hennebert J, Petrovska-delacretaz D, Humm A, Evequoz F, Ingold R, Rotz DV (2005) MyIDEA–Multimodal Biometrics Database, Description of Acquisition Protocols. Biometrics on the Internet, 3rd COST 275 workshop, Hatfield, pp 59–62

    Google Scholar 

  11. Eskandari M, Sharifi O (2017) Optimum scheme selection for face–Iris biometric. IET Biometrics. Volume 6, Issue 5, September 2017, p. 334–341. https://doi.org/10.1049/iet-bmt.2016.0060

  12. Fierrez J, Ortega-Garcia J et al (2007) BioSec baseline Corpus: a multimodal biometric database. Pattern Recognition, Volume 40, Issue 4,2007, Pages 1389–1392, ISSN 0031-3203. https://doi.org/10.1016/j.patcog.2006.10.014

  13. Fierrez J, Galbally J, Ortega-Garcia J, Freire MR, Alonso-Fernandez F, Ramos D, Toledano DT, Gonzalez-Rodriguez J, Siguenza JA, Garrido-Salas J, Anguiano E, Gonzalez-de-Rivera G, Ribalda R, Faundez-Zanuy M, Ortega JA, Cardenoso-Payo V, Viloria A, Vivaracho CE, Moro QI, Igarza JJ, Sanchez J, Hernaez I, Orrite-Urunuela C, Martinez-Contreras F, Gracia-Roche JJ (2010) BiosecurID: A Multimodal Biometric Database. Pattern Anal Appl 13, 235–246. doi: https://doi.org/10.1007/s10044-009-0151-4, 2010

  14. Gross R, Shi J (2001) The CMU motion of body (MoBo) database. Technical Report. CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University, June, 2001 

  15. Hassaballah M, Aly S (2015) Face recognition: challenges, achievements and future directions. IET Computer Vision 9(4):614–626. https://doi.org/10.1049/iet-cvi.2014.0084

    Article  Google Scholar 

  16. Hassaballah M, Murakami K, Ido S (2011) An automatic eye detection method for gray intensity facial images. Int J Computer Sci Issues (IJCSI) 8(4):272

    Google Scholar 

  17. Hassaballah M, Murakami K, Ido S (2011) eye and nose fields detection from gray scale facial images. MVA2011 IAPR conference on machine vision applications, June 13-15, 2011, Nara, JAPAN.

  18. Hassaballah M, Bekhet S, Rashed AAM, Zhang G (2019) Facial features detection and localization. In: Hassaballah M, Hosny K (eds) recent advances in computer vision. Studies in computational intelligence, vol 804. Springer, Cham. https://doi.org/10.1007/978-3-030-03000-1_2

    Chapter  Google Scholar 

  19. He L, Li and Qi Zhang and Zhenan sun (2019) dynamic feature matching for partial face recognition. IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 791–802, Feb. 2019. https://doi.org/10.1109/TIP.2018.2870946

  20. Ho CC, Hu N, Tan W-H, Ng K-W, Tong H-L, Yap TT-V, Pei-Fen C, Eswaran C, Abdullah J (2013) MMU645GASPFA: A COTS multimodal biometric database. Pattern Recognition Letters. Volume 34, Issue 15, Pages 2043–2050

  21. Hong S, Ryu J (2020) Unsupervised Face Domain Transfer for Low-Resolution Face Recognition. IEEE Signal Processing Letters 27:156–160. https://doi.org/10.1109/LSP.2019.2963001

    Article  Google Scholar 

  22. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, tech. Rep. 07–49, October, 2007

  23. CASIA-IrisV3 Database, http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp

  24. Jonathon PP, Flynn PJ, Scruggs T, Bowyer KW, Worek W (2006) preliminary face recognition grand challenge results. Proceedings of the 7th international conference on automatic face and gesture recognition (FGR’06), 0-7695-2503-2/06, IEEE

  25. Vishwanath C. Kagawade, Shanmukhappa A. Angadi (2019) Multi-directional local gradient descriptor: A new feature descriptor for face recognition. Image and Vision Computing, Volumes 83–84, 2019, Pages 39–50, ISSN 0262-8856.https://doi.org/10.1016/j.imavis.2019.02.001

  26. Khalaf ET, Mohammad MN, Moorthy K (2018) Robust Partitioning and Indexing for Iris Biometric Database based on Local Features. IET Biometric 7(6):589597

    Article  Google Scholar 

  27. Khan MT, Arora D, Shukla S (2013) Feature Extraction through Iris Images using 1-D Gabor Filter on Different Iris Datasets. 978–1–4799-0192-0, 2013 IEEE

  28. Khanna, Weicheng S (1994) Automated Fingerprint Identification System (AFIS) Benchmarking using the National Institute of Standards and Technology (NIST) Special Database 4. IEEE, pp 188 194

  29. Li P, Prieto L, Mery D, Flynn PJ (2019) On low-resolution face recognition in the wild: comparisons and new techniques. IEEE Transactions on Information Forensics and Security 14(8):2000–2012. https://doi.org/10.1109/TIFS.2018.2890812

    Article  Google Scholar 

  30. Liu Z, Luo P, Wang X, Tang X (2015) deep learning face attributes in the wild. International Conference on Computer Vision (ICCV), December, 2015

  31. Liu M, Zhou Z, Shang P, Xu D (2020) Fuzzified Image Enhancement for Deep Learning in Iris Recognition. IEEE Trans Fuzzy Syst 28(1):92–99. https://doi.org/10.1109/TFUZZ.2019.2912576

    Article  Google Scholar 

  32. Lu X, Fang Z, Xu T, Zhang H, Tuo H (2015) Efficient image categorization with sparse Fisher vector. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, Australia, 2015, pp. 1498–1502. https://doi.org/10.1109/ICASSP.2015.7178220

  33. Lu X, Wang W, Ma C, Shen J, Shao L, Porikli F (2019) See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks. CVPR2019, arXiv:2001.06810

  34. Lu X, Ma C, Ni B, Yang X (2019) Adaptive region proposal with channel regularization for robust object tracking. IEEE Trans Circuits Syst Video Technol:1. https://doi.org/10.1109/TCSVT.2019.2944654

  35. Luo Z, Gu Q, Qi G, Liu S, Zhu Y, Bai Z (2019) A Robust Single-Sensor Face and Iris Biometric Identification System Based on Multimodal Feature Extraction Network. 2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI), Portland, pp 1237–1244. https://doi.org/10.1109/ICTAI.2019.00-95

    Book  Google Scholar 

  36. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK(2002) Second fingerprint verification competition. IEEE computer society FVC2002, pp 811–814

  37. Martinez AM, Benavente R (1998) The AR database, CVC technical report

  38. MMU database (2021): http://www.cs.princeton.edu/andyz/irisrecognition

  39. Mokhayeri F, Granger E, Bilodeau G (2019) Domain-specific face synthesis for video face recognition from a single sample per person. IEEE Trans Inform Forensics Secur, vol. 14, no. 3, pp. 757–772, March 2019. doi: https://doi.org/10.1109/TIFS.2018.2866295

  40. Nguyen K, Fookes C, Sridharan S (2020) Constrained Design of Deep Iris Networks. IEEE Transactions on Image Processing, vol. 29, pp. 7166–7175, 2020. https://doi.org/10.1109/TIP.2020.2999211

  41. Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Worek W (2006) Preliminary Face Recognition Grand Challenge Results. 7th International Conference on Automatic Face and Gesture Recognition (FGR06), Southampton, UK, 2006, pp. 15–24. https://doi.org/10.1109/FGR.2006.87

  42. Oliveira HP, Magalhães F (2012) Two Unconstrained Biometric Databases. Image Analysis and Recognition, ICIAR 2012. Lecture notes in computer science, vol 7325. Springer, Berlin

    Google Scholar 

  43. Ortega-Garcia J, Fierrez J, Alonso-Fernandez F, Galbally J, Freire MR, Gonzalez-Rodriguez J, Garcia-Mateo C, Alba-Castro J-L, Gonzalez-Agulla E, Otero Muras E, Garcia-Salicetti S, Allano L, Ly-Van B, Dorizzi B, Kittler J, Bourlai T, Poh N, Deravi F, Ng M, Fairhurst M, Hennebert J, Humm A, Tistarelli M, Brodo L, Richiardi J, Drygajlo A, Ganster H, Sukno FM, Pavani SK, Frangi A, Akarun L, Savran A (2010) The multi scenario multi environment Biosecure multimodal database (BMDB). IEEE Trans Pattern Anal Machine Intelligence, Vol. 32, 1097–1111

  44. Poornima S (2016) Multimodal database: biometric authentication for unconstrained samples. First international conference on information and communication Technology for Intelligent Systems, Vol. 2. Smart innovation, systems and technologies, Vol. 51, springer

  45. Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1615–1618, Dec. 2003. https://doi.org/10.1109/TPAMI.2003.1251154

  46. Štruc V, Pavešic N (2009) Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition. Volume 20, Issue 1 (2009), pp. 115–138. https://doi.org/10.15388/Informatica.2009.240 

  47. Te Chu C, Chen CH (2005) High Performance Iris Recognition Based on LDA and LPCC. Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05), 1082–3409/05

  48. Dobeš M, Martinek J, Skoupil D, Dobešová Z, Pospíšil J (2006) Human eye localization using the modified Hough transform. Optik, Volume 117, No.10, p.468–473, Elsevier 2006, ISSN 0030-4026

  49. Wenguan Wang, Xiankai Lu, Jianbing Shen, David J. Crandall, Ling Shao (2019) Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks.  Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 9236–9245

  50. Wang C, Muhammad J, Wang Y, He Z, Sun Z (2020) Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition. IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2944–2959, 2020. https://doi.org/10.1109/TIFS.2020.2980791

  51. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. Pattern Anal. Mach. Intelligence, 2001, volume 23, number 6, pages 643–660

  52. Yang H, Han X (2020) Face Recognition Attendance System Based on Real-Time Video Processing. IEEE Access 8:159143–159150. https://doi.org/10.1109/ACCESS.2020.3007205

    Article  Google Scholar 

  53. Zhang X, Dail Y, Xu X (2017) Android-based Multimodal Biometric Identification Systems using Feature Level Fusion. International Symposium on Intelligent Signal Processing and communication Systems, November 6–9, 2017, 978–1–5386-2/17, Xiamen, China

  54. Zhao D, Luo W, Liu R, Yue L (2018) Negative Iris Recognition. IEEE Transactions on Dependable and Secure Computing, Vol. 15, No. 1, January/February 2018

  55. Zheng S, Zhang J, Huang K, He R, Tan T (2011) Robust view transformation model for gait recognition. 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium, 2011, pp. 2073–2076. https://doi.org/10.1109/ICIP.2011.6115889

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, Basaveshwar Engineering College, Bagalkot, India

    Vishwanath C. Kagawade

  2. Department of Computer Science and Engineering, Centre for Post Graduate Studies, VTU, Belagavi, India

    Shanmukhappa A. Angadi

Authors
  1. Vishwanath C. Kagawade
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shanmukhappa A. Angadi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Vishwanath C. Kagawade.

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

Kagawade, V.C., Angadi, S.A. VISA: a multimodal database of face and iris traits. Multimed Tools Appl 80, 21615–21650 (2021). https://doi.org/10.1007/s11042-021-10650-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10650-4

Keywords