Skip to main content

Advertisement

SpringerLink
Log in

Review on secure traditional and machine learning algorithms for age prediction using IRIS image

  • 1204: Multimedia Technology for Security and Surveillance in Degraded Vision
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Iris recognition is a secure and best-chosen biometric application in the digital world because of its unique characteristics. Day by day, the digital world plays a significant role in human life for various applications. The applications are vastly spread over secure applications of the nation such as border control applications, criminal investigations, postmortem studies, access the digital equipment, smart homes, smart appliances, smart cars etc. Due to the digitalization of the world, all the research communities, scientists, and industries are focusing on the biometric-based secured iris recognition system. Several researchers have done much work in this domain, but there is still a scope of improvement for various reasons, i.e., less speed and accuracy of the module. The researcher has implemented various algorithms based on traditional and neural network architectures. In this scenario, this paper gives a brief on different techniques and algorithms used by researchers to predict the age of human people using the iris. This paper discussed one hundred and one papers in the literature with various image segmentation, feature extraction and classification of the iris. This paper summarizes publicly available standard databases and various evaluation parameters, i.e., accuracy, precision, recall, f-score, etc. The research community evaluated the age prediction through the iris-based state-of-the-art algorithms with secure prediction, i.e., TPR, TNR, FPR, FNR. Finally, this paper provides the strengths and weaknesses of the various state of art algorithms, respectively, and summarizes the gaps in the existing technology with the scope of improvement.

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
Fig. 7

Similar content being viewed by others

Abbreviations

ACC:

Accuracy

AGF:

Adjusted F-Measure

ANN:

Artificial Neural Network

ANOVA:

Analysis of Variance

ARG:

Average Relative Growth

CASIA:

Chinese Academy of Science

CASIA:

Chinese Academy of Science, Institute of Automation

CHT:

Circular Hough Transform

DWT:

Discrete Wavelet Transform

EDM:

Eccentric Distance Measure

ERR:

Error Rate

FDR:

False Discovery Rate

F-Measure:

F-Measure, Recall

FNR:

False Negative Rate

FOR:

False Omission Rate

FPR:

False Positive Rate

GM:

Geometric Mean

HD:

Hamming Distance

IER:

Iris Effective Region

LBP/LVQ:

Local Binary Pattern/ Learning Vector Quantization

LR+/LR:

Positive Likelihood, Negative Likelihood

MMU:

Multimedia University

MMU:

Multimedia University

NA:

Negative Accuracy

NIR:

Near-Infrared Spectroscopy

NPV:

Negative Prediction Value

OP:

Optimization Precision

PCA:

SIFT- principal components analysis- Scale Invariant Feature Transform

PPV:

Positive Prediction Value or Precision

ROC:

Receiving Operating Characteristics

ROI:

Region of Interest

ROI:

Region of Interest

SIFT:

Scale Invariant Feature Transform

SURF:

Speeded Up Robust Feature

SVD:

Single Value Decomposition

TNR:

True Negative Rate

TPR:

True Positive Rate

UBIRIS:

University of Beira Interior

References

  1. Abbasi A, Khan M (2016) Iris-pupil thickness based method for determining age Group of a Person. Int Arab J Inform Technol (IAJIT) 13(6)

  2. Abdullah, Mohammed AM, Dlay SS, Woo WL, Chambers JA (2016) Robust iris segmentation method based on a new active contour force with a noncircular normalization. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47(12):3128–3141

  3. Al-Allaf O, AL-Allaf A, Tamimi AA, AbdAlKader SA (2012) Artificial neural networks for iris recognition system: comparisons between different models, architectures and algorithms. International Journal of Information and Communication Technology Research 2(10)

  4. Al-Allaf ON, Ahmad SA, AlKader A, Tamimi AA (2013) Pattern recognition neural network for improving the performance of iris recognition system. J Sci Eng Res 4(6):661–667

    Google Scholar 

  5. Alonso-Fernandez F, Tome-Gonzalez P, Ruiz-Albacete V, Ortega-Garcia J (2009) "Iris recognition based on SIFT features," 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS), pp. 1–8. https://doi.org/10.1109/BIDS.2009.5507529.

  6. Alrifaee, Mustafa M (2020) "A Short Survey of IRIS Images Databases." Available at SSRN 3616735.

  7. Alvarez-Betancourt Y, Garcia-Silvente M (2016) A keypoints-based feature extraction method for iris recognition under variable image quality conditions. Knowl-Based Syst 92:169–182

    Article  Google Scholar 

  8. Anderson JE (1956) Psychological aspects of aging. In conference on planning research Apr 1955, Bethesda, MD, US. American Psychological Association

  9. Baker SE, Bowyer KW, Flynn PJ (2009) Empirical evidence for correct iris match score degradation with increased time-lapse between gallery and probe matches. In: International Conference on Biometrics. Springer, Berlin, Heidelberg, pp 1170–1179

    Google Scholar 

  10. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-Up Robust Features (SURF). Comput Vis Image Underst 110(3):346–359. https://doi.org/10.1016/j.cviu.007.09.014

    Article  Google Scholar 

  11. Bazrafkan S, Thavalengal S, Corcoran P (2018) An end to end deep neural network for iris segmentation in unconstrained scenarios. Neural Netw 106:79–95

    Article  Google Scholar 

  12. Belcher C, Yingzi D (2008) A selective feature information approach for iris image-quality measure. IEEE Transac Inform Forens Sec 3(3):572–577

    Article  Google Scholar 

  13. Belcher C, Yingzi D (2009) Region-based SIFT approach to iris recognition. Opt Lasers Eng 47(1):139–147

    Article  Google Scholar 

  14. Bennet, J (2014) Chilambuchelvan Arul Ganaprakasam, and Kannan Arputharaj. "A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis." The Scientific world journal 2014.

  15. Bharadwaj S, Bhatt HS, Vatsa M, Singh R (2010) "Periocular biometrics: When iris recognition fails." In 2010 fourth IEEE international conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6. IEEE

  16. BioSecuredatabase : Ortega-Garcia J, Alonso-Fernandez F, Fierrez-Aguilar J, Garcia-Mateo C, Salicetti S, Allano L, Ly-Van B, Dorizzi B (2006) Software tool and acquisition equipment

  17. Bowyer KW, Hollingsworth K, Flynn PJ (2008) Image understanding for iris biometrics: a survey. Comput Vis ImageUnderst 110(2):281–307

  18. Burt D, Michael, Perrett DI (1995) Perception of age in adult Caucasian male faces: computer graphic manipulation of shape and colour information. Proc R Soc Lond Ser B Biol Sci 259(1355):137–143

    Article  Google Scholar 

  19. Cakmak HB, Cagil N, Simavli H, Raza S (2012) Corneal white-to-white distance and mesopic pupil diameter. Int J Ophthalmol 5(4):505

    Google Scholar 

  20. Chen J, Shen F, Chen DZ, Flynn PJ (2016) Iris recognition based on human-interpretable features. IEEE Transac Inform Forens Sec 11(7):1476–1485

    Article  Google Scholar 

  21. Daugman J (1994) Biometric personal identification system based on iris analysis, US Patent No 5,291,560 Washington, DC. Patent and Trademark Office.

  22. Daugman J (2002) How iris recognition works. IEEE Int Conf Image Proc 1:I/33–I/36

    Google Scholar 

  23. Daugman, John G High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11, 1993):1148–1161

  24. Dehkordi, AB, Abu-Bakar SAR (2015) "Iris code matching using adaptive Hamming distance." In 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 404–408. IEEE

  25. Dey S, Samanta D (2010) Fast and accurate personal identification based on iris biometric. Int J Biomet 2(3):250–281

    Article  Google Scholar 

  26. Dubbelman M, Sicam VADP, Van der Heijde GL (2006) The shape of the anterior and posterior surface of the aging human cornea. Vis Res 46(6–7):993–1001

    Article  Google Scholar 

  27. Elminir HK, Azzam YA, Younes FI (2007) Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models. Energy 32(8):1513–1523

    Article  Google Scholar 

  28. Erbilek M, Fairhurst M, Abreu MCDC (2013) "Age prediction from iris biometrics." In 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013), pp. 1–5. IET

  29. Erbilek M, Fairhurst M, and Da Costa-Abreu M (2014) Improved age prediction from biometric data using multimodal configurations. In 2014 international conference of the biometrics special interest group (BIOSIG), pp. 1–7. IEEE.

  30. Fairhurst M, Erbilek M (2011) Analysis of physical ageing effects in iris biometrics. IET Comput Vis 5(6):358–366

    Article  Google Scholar 

  31. Fang Y, Wang Z (2008) "Improving LBP features for gender classification." In 2008 International Conference on Wavelet Analysis and Pattern Recognition, vol. 1, pp. 373–377. IEEE.

  32. Fenker SP, Bowyer KW (2011) "Experimental evidence of a template aging effect in iris biometrics." In 2011 IEEE Workshop on Applications of Computer Vision (WACV), pp. 232–239. IEEE.

  33. Fenker SP, Bowyer KW (2012) "Analysis of template aging in iris biometrics." In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 45–51. IEEE.

  34. Fledelius HC, Stubgaard M (1986) Changes in eye position during growth and adult life: as based on exophthalmometry, interpupillary distance, and orbital distance measurements. Acta Ophthalmol 64(5):481–486

    Article  Google Scholar 

  35. Gopikrishnan M, Santhanam T (2011) Effect of different neural networks on the accuracy in iris patterns recognition. Int J Rev Comput 7:22–28

    Google Scholar 

  36. Gowroju, S, Kumar S(2020) Robust deep learning technique: U-Net architecture for pupil segmentation. In 2020 11th IEEE annual information technology, electronics and mobile communication conference (IEMCON), pp. 0609–0613. IEEE.

  37. Gowroju S, Kumar S (2021) "Robust Pupil Segmentation using UNET and Morphological Image Processing." In 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 105–109. IEEE.

  38. He Z, Tan T, Sun Z, Qiu X (2008) Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans Pattern Anal Mach Intell 31(9):1670–1684

    Google Scholar 

  39. Hollingsworth K, Bowyer KW, Flynn PJ (2009) Pupil dilation degrades iris biometric performance. Comput Vis Image Underst 113(1):150–157

    Article  Google Scholar 

  40. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A 79:2554–2558

    Article  MathSciNet  MATH  Google Scholar 

  41. Hussain, Jawad S (2021) Age classification using convolution neural networks using a local dataset. Turkish J Comput Math Educ (TURCOMAT) 12(9):2038–2045

    Google Scholar 

  42. Ibrahim, Talal M, Khan TM, Khan MA, and Guan L (2010) "Automatic segmentation of pupil using local histogram and standard deviation." In Visual Communications and Image Processing 2010, vol. 7744, p. 77442S. International Society for Optics and Photonics.

  43. Ince, Furkan I, Erdem YS, Bulut F, Sharif MH (2019) A low-cost pupil center localization algorithm based on maximized integral voting of circular hollow kernels. Comput J 62(7):1001–1015

    Article  Google Scholar 

  44. Jones K, Johnson KA, Becker JA, Spiers PA, Albert MS, Holman BL (1998) Use of singular value decomposition to characterize age and gender differences in SPECT cerebral perfusion. J Nucl Med 39(6):965–973

    Google Scholar 

  45. Kadlecova V, Peleška M, Vaško A (1958) Dependence on age of the diameter of the pupil in the dark. Nature 182(4648):1520–1521

    Article  Google Scholar 

  46. Kellokumpu V, Zhao G, Pietikäinen M (2011) Recognition of human actions using texture descriptors. Mach Vis Appl 22(5):767–780

    Article  Google Scholar 

  47. Khobragade K, Kale KV (2014) A new technique for fast and accurate Iris localization. Int J Innov Eng Technol (IJIET), ISSN: 2319–1058

  48. Kiranmayee BV, Swathi A (2011) KGiSL,“ Driver Fatigue Monitoring and Detection System Based on Pupil Analysis," KGisl Proceedings.

  49. Ko JG, Gil YH, Yoo JH, Chung KIL (2007) A novel and efficient feature extraction method for iris recognition. ETRI J 29(3):399–401

    Article  Google Scholar 

  50. Kohail SN (2012) Using artificial neural network for human age estimation based on facial images. In 2012 international conference on innovations in information technology (IIT), pp. 215–219. IEEE

  51. Krichen E, Garcia-Salicetti S, Dorizzi B (2009) A new phase-correlation-based iris matching for degraded images. IEEE Transac Syst, Man, Cybern, Part B (Cybernetics) 39(4):924–934

    Article  Google Scholar 

  52. Kumar S, Singh S, Kumar J (2018) Live detection of face using machine learning with multi-feature method. Wirel Pers Commun 103(3):2353–2375

    Article  Google Scholar 

  53. Kumar S, Singh S, Kumar J (2018) Automatic live facial expression detection using genetic algorithm with Haar wavelet features and SVM. Wirel Pers Commun 103(3):2435–2453

    Article  Google Scholar 

  54. Kumar S, Lamba VK, Jangra S (2019) Existing and Emerging Covariates of Iris Recognition. Int J Comput Sci Eng:140–146

  55. Kumar S, Singh S, Kumar J (2019) "Gender Classification Using Machine Learning with Multi-Feature Method." In IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0648–0653, Las Vegas, USA, January 7th–9th

  56. Kumar S, Singh S, Kumar J (2021) Face spoofing detection using improved SegNet architecture with a blur estimation technique. Int J Biometrics 13(2–3):131–149

    Article  Google Scholar 

  57. Lanitis A (2010) A survey of the effects of aging on biometric identity verification. Int J Biomet 2(1):34–52

    Article  Google Scholar 

  58. Li SZ, Chu RF, Liao SC, Zhang L (2007) Illumination invariant face recognition using near-infrared images. IEEE Trans Pattern Anal Mach Intell 29(4):627–639

    Article  Google Scholar 

  59. Li P, Liu X, Xiao L, Song Q (2010) Robust and accurate iris segmentation in very noisy iris images. Image Vis Comput 28(2):246–253

    Article  Google Scholar 

  60. Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118

    Article  MathSciNet  MATH  Google Scholar 

  61. Liu L, Lao S, Fieguth PW, Guo Y, Wang X, Pietikäinen M (2016) Median robust extended local binary pattern for texture classification. IEEE Trans Image Process 25(3):1368–1381

    Article  MathSciNet  MATH  Google Scholar 

  62. Liu L, Fieguth P, Wang X, Pietikäinen M, Dewen H (2016) Evaluation of LBP and deep texture descriptors with a new robustness benchmark. In European Conference on Computer Vision. Springer, Cham, pp 69–86

    Google Scholar 

  63. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

  64. Ma L, Wang Y, Tan T (2002) "Iris recognition using circular symmetric filters." In Object recognition supported by user interaction for service robots, vol. 2, pp. 414–417. IEEE.

  65. Ma L, Tan T, Wang Y, Zhang D (2004) Efficient iris recognition by characterizing key local variations. IEEE Trans Image Process 13(6):739–750

    Article  Google Scholar 

  66. Machado, Palhares CE, Flores MRP, Lima LNC, Tinoco RLR, Franco A, Bezerra ACB, Evison MP, Guimarães MA (2017) A new approach for the analysis of facial growth and age estimation: Iris ratio. PLoS One 12(7):e0180330

    Article  Google Scholar 

  67. MacLachlan C, Howland HC (2002) Normal values and standard deviations for pupil diameter and interpupillary distance in subjects aged 1 month to 19 years. Ophthalmic Physiol Opt 22(3):175–182

    Article  Google Scholar 

  68. Masek L (2003) Recognition of human iris patterns for biometric identification (Doctoral dissertation, master’s thesis, University of Western Australia)

  69. Mikolajczyk K, Schmid C (2004) Scale & affine invariant interest point detectors. Int J Comput Vis 60(1):63–86

    Article  Google Scholar 

  70. Miyazawa K, Ito K, Aoki T, Kobayashi K, Nakajima H (2008) An effective approach for iris recognition using phase-based image matching. IEEE Trans Pattern Anal Mach Intell 30(10):1741–1756

    Article  Google Scholar 

  71. Monro DM, Rakshit S, Zhang D (2007) DCT-based iris recognition. IEEE Trans Pattern Anal Mach Intell 29(4):586–595

    Article  Google Scholar 

  72. Morrison, Patrick J (2010) "The iris–a window into the genetics of common and rare eye diseases." The Ulster medical journal 79, no. 1

  73. Ng TW, Tay TL, Khor SW (2010) Iris recognition using rapid Haar wavelet decomposition. In 2010 2nd international conference on signal processing systems. IEEE 1:1–820

  74. Nkengne A, Bertin C, Stamatas GN, Giron A, Rossi A, Issachar N, Fertil B (2008) Influence of facial skin attributes on the perceived age of Caucasian women. J Eur Acad Dermatol Venereol 22(8):982–991

    Article  Google Scholar 

  75. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  76. Ortiz E, Bowyer KW, Flynn PJ (2013) A linear regression analysis of the effects of age related pupil dilation change in iris biometrics. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS). IEEE 1–6

  77. Phuong HM, Dung L, de Souza-Daw T, Dzung NT, Hoang TM (2012) "Extraction of human facial features based on Haar feature with Adaboost and image recognition techniques." In 2012 Fourth International Conference on Communications and Electronics (ICCE), pp. 302–305. IEEE

  78. Proença H, Alexandre LA (2005) UBIRIS: A noisy iris image database. In International Conference on Image Analysis and Processing, pp. 970–977. Springer, Berlin, Heidelberg

    Google Scholar 

  79. Proença H, Alexandre LA (2006) Iris segmentation methodology for non-cooperative recognition. IEE ProcVision, Image Signal Proces 153(2):199–205

    Article  Google Scholar 

  80. Rapaka S, Pullakura R, Mandelli J (2018) A new approach for non-ideal iris segmentation using fuzzy C-means clustering based on particle swarm optimization. Asian J Engin App Technol 7(2):42–45

    Google Scholar 

  81. Rathgeb C, Uhl A (2010) Secure iris recognition based on local intensity variations. In International Conference Image Analysis and Recognition, pp. 266–275. Springer, Berlin, Heidelberg

    Google Scholar 

  82. Romer AS, Th S. Parsons (1977) "The vertebrate body. Philadelphia." PA: Holt-Saunders International. 555–56.

  83. Said FS, Sawires WS (1972) Age dependence of changes in pupil diameter in the dark. Optica Acta: Int J Opt 19(5):359–361

    Article  Google Scholar 

  84. Salvi SM, Akhtar S, Currie Z (2006) Cambios de envejecimiento en el ojo. Postgrad Med J 82:581–587

    Article  Google Scholar 

  85. Seitz R (1957) The dependence on age of the dilation of the dark-adapted pupil. Klin Monatsbl Augenheilkd 131:48–56

    Google Scholar 

  86. Sgroi A, Bowyer KW, Flynn PJ (2013) "The prediction of old and young subjects from iris texture." In 2013 International Conference on Biometrics (ICB). IEEE 1–5.

  87. Shah S, Ross A (2009) Iris segmentation using geodesic active contours. IEEE Transac Inform Forens Sec 4(4):824–836

    Article  Google Scholar 

  88. Shekar BH, Bhat SS (2017) Multi-Patches iris based person authentication system using particle swarm optimization and fuzzy C-means clustering. Int Archi Photogram, Remote Sens Spatial Inform Sci 42:243

    Article  Google Scholar 

  89. Shrimal G, Rathi R (2013) IRIS identification based on multilayer feed forward neural network. IJLTEMAS 21–25

  90. Sung H, Lim J, Park J, Lee Y (2004) Iris recognition using collarette boundary localization. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR). IEE 4:857–860

  91. Swathi A, Kumar S (2021) A smart application to detect pupil for small dataset with low illumination. Innov Syst Softw Eng 17(1):29–43

    Article  Google Scholar 

  92. Swathi A, Kumar S (2021) Review on pupil segmentation using CNN-region of interest. In Intelligent Communication and Automation Systems, pp. 157–168. CRC Press.

  93. Tan T, He Z, Sun Z (2010) Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vis Comput 28(2):223–230

    Article  Google Scholar 

  94. Tanti M, Gatt A, Camilleri KP (2017) What is the role of recurrent neural networks (rnns) in an image caption generator?. arXiv preprint arXiv:1708.02043

  95. Thalji Z, Alsmadi M (2013) Iris recognition using robust algorithm for eyelid, eyelash and shadow avoiding. World Appl Sci J 25(6):858–865

    Google Scholar 

  96. Thompson HS (Ed.) (1979) Topics in neuro-ophthalmolgy. Williams & Wilkins, Baltimore, pp. 124–150

  97. Trokielewicz M, Czajka A, Maciejewicz P (2018) Iris recognition under biologically troublesome conditions-effects of aging, diseases and post-mortem changes. arXiv preprint arXiv:1809.00182

  98. Wang J, Yang Y, Mao J, Huang Z, Huang C, Wei X (2016) "Cnn-rnn: A unified framework for multi-label image classification." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2285–2294.

  99. Wildes, Richard P (1997) Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85 (9):1348–1363

    Google Scholar 

  100. Zafar, Faisal M, Zaheer Z, Khurshid J (2013) Novel iris segmentation and recognition system for human identification. In Proceedings of 2013 10th International Bhurban Conference on Applied Sciences & Technology (IBCAST), pp. 128–131. IEEE

Download references

Author information

Authors and Affiliations

  1. Lovely Professional University, Sreyas Institute of Engineering and Technology, Hyderabad, Punjab, India

    Swathi Gowroju

  2. Lovely Professional University, Punjab, India

    Aarti

  3. Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

    Sandeep Kumar

Authors
  1. Swathi Gowroju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aarti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sandeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swathi Gowroju.

Ethics declarations

Conflict of interest

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

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

Gowroju, S., Aarti & Kumar, S. Review on secure traditional and machine learning algorithms for age prediction using IRIS image. Multimed Tools Appl 81, 35503–35531 (2022). https://doi.org/10.1007/s11042-022-13355-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13355-4

Keywords

Search

Navigation