References

• 1. 1)
     • 34. Uhl, A., Marcel, S., Busch, C., et al: ‘Handbook of vascular biometrics’ (Springer, USA, 2020). forthcoming.
  2. 2)
     • 90. Chaari, A., Lelandais, S., Ahmed, M.B.: ‘A pruning approach improving face identification systems’. Int. Conf. on Advanced Video and Signal Based Surveillance, Boston, USA, 2009, pp. 85–90.
  3. 3)
     • 167. García, G.J., Jara, C.A., Pomares, J., et al: ‘A survey on FPGA-based sensor systems: towards intelligent and reconfigurable low-power sensors for computer vision, control and signal processing’, Sensors, 2014, 14, (4), pp. 6247–6278.
  4. 4)
     • 47. Paulino, A.A., Liu, E., Cao, K., et al: ‘Latent fingerprint indexing: fusion of level 1 and level 2 features’. Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington D.C., USA, 2013, pp. 1–8.
  5. 5)
     • 82. Zhou, Y., Liu, Y., Feng, Q., et al: ‘Palm-vein classification based on principal orientation features’, PLOS One, 2014, 9, (11), pp. 1–12.
  6. 6)
     • 15. ISO/IEC JTC1 SC37 Biometrics: ‘ISO/IEC 2382-37:2017. Information technology – vocabulary – part 37: biometrics’ (International Organization for Standardization, 2017).
  7. 7)
     • 87. Iloanusi, O.N., Gyaourova, A., Ross, A.: ‘Indexing fingerprints using minutiae quadruplets’. Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), Colorado Springs, USA, 2011, pp. 127–133.
  8. 8)
     • 109. Aggarwal, C.C., Reddy, C.K.: ‘Data clustering: algorithms and applications’ (Chapman & Hall/CRC, USA, 2013).
  9. 9)
     • 112. Mehrotra, H., Srinivas, B.G., Majhi, B.: ‘Indexing iris biometric database using energy histogram of DCT subbands’, J. Commun. Comput. Inf. Sci., 2009, 40, pp. 194–204.
  10. 10)
      • 177. Mittal, S., Vetter, J.S.: ‘A survey of CPU-GPU heterogeneous computing techniques’, Comput. Surv., 2015, 47, (4), pp. 1–35.
  11. 11)
      • 29. Li, S.Z., Jain, A.K.: ‘Encyclopedia of biometrics’ (Springer, USA, 2015).
  12. 12)
      • 62. Billeb, S., Rathgeb, C., Buschbeck, M., et al: ‘Efficient two-stage speaker identification based on universal background models’. Int. Conf. of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 2014, pp. 1–6.
  13. 13)
      • 25. Abbasifard, M.R., Ghahremani, B., Naderi, H.: ‘A survey on nearest neighbor search methods’, Int. J. Comput. Appl., 2014, 95, (25), pp. 39–52.
  14. 14)
      • 151. Damer, N., Terhörst, P., Braun, A., et al: ‘Efficient, accurate, and rotation-invariant iris code’, Signal Process. Lett., 2017, 24, (8), pp. 1233–1237.
  15. 15)
      • 196. Simmhan, Y., Shukla, A., Verma, A.: ‘Benchmarking fast-data platforms for the Aadhaar biometric database’. Big Data Benchmarking, Potsdam, Germany, 2015, pp. 21–39.
  16. 16)
      • 165. Andina, J.J.R., De la Torre Arnanz, E., Valdes, M.D.: ‘FPGAs: fundamentals, advanced features, and applications in industrial electronics’ (CRC Press, USA, 2017).
  17. 17)
      • 35. Proença, H., Neves, J.C.: ‘Security’, in Rathgeb, C., Busch, C. (Eds.): ‘Iris and Periocular Recognition’ (Institution of Engineering and Technology, UK, 2017), pp. 101–124.
  18. 18)
      • 116. Rathgeb, C., Breitinger, F., Baier, H., et al: ‘Towards bloom filter-based indexing of iris biometric data’. Int. Conf. on Biometrics (ICB), Phuket, Thailand, 2015, pp. 422–429.
  19. 19)
      • 169. Rakvic, R.N., Ulis, B.J., Broussard, R.P., et al: ‘Parallelizing iris recognition’, Trans. Inf. Forensics Secur., 2009, 4, (4), pp. 812–823.
  20. 20)
      • 92. Mukherjee, R., Ross, A.: ‘Indexing iris images’. Int. Conf. on Pattern Recognition (ICPR), Tampa, USA, 2008, pp. 1–3.
  21. 21)
      • 180. Li, X., Zhang, G., Huang, H.H., et al: ‘Performance analysis of GPU-based convolutional neural networks’. Int. Conf. on Parallel Processing (ICPP), Philadelphia, USA, 2016, pp. 67–76.
  22. 22)
      • 64. Iqbal, A., Namboodiri, A.: ‘Cascaded filtering for fingerprint identification using random projections’. Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), Rhode Island, 2012, pp. 77–82.
  23. 23)
      • 163. Saegusa, T., Maruyama, T., Yamaguchi, Y.: ‘How fast is an FPGA in image processing?’. Int. Conf. on Field Programmable Logic and Applications, Heidelberg, Germany, 2008, pp. 77–82.
  24. 24)
      • 138. Knuth, D.: ‘Sorting and searching’, Art Comput. Program., (Addison-Wesley, USA, 1998), 3.
  25. 25)
      • 73. Heindl, R.: ‘Daktyloskopie’ (W. de Gruyter & Company, Germany, 1927).
  26. 26)
      • 96. Mhatre, A.J., Palla, S., Chikkerur, S., et al: ‘Efficient search and retrieval in biometric databases’. Biometric Technology for Human Identification II, Orlando, USA, 2005, vol. 5779, pp. 265–274.
  27. 27)
      • 95. Raghavendra, R., Surbiryala, J., Busch, C.: ‘An efficient finger vein indexing scheme based on unsupervised clustering’. Int. Conf. on Identity, Security and Behavior Analysis (ISBA), Hong Kong, 2015, pp. 1–8.
  28. 28)
      • 100. Galar, M., Derrac, J., Peralta, D., et al: ‘A survey of fingerprint classification part I: taxonomies on feature extraction methods and learning models’, Knowl.-Based Syst., 2015, 81, pp. 76–97.
  29. 29)
      • 7. European Union Agency for the Operational Management of Large-Scale IT Systems in the Area of Freedom, Security and Justice: ‘Eurodac storage capacity increased’, eu-LISA, 2016. Available at: https://www.eulisa.europa.eu/Newsroom/News/Pages/Eurodacstorage-capacity-increased.aspx, accessed 11 6 2019.
  30. 30)
      • 38. Ratha, N.K., Karu, K., Chen, S., et al: ‘A real-time matching system for large fingerprint databases’, Trans. Pattern Anal. Mach. Intell., 1996, 18, (8), pp. 799–813.
  31. 31)
      • 186. Rife, D.C.: ‘Finger prints as criteria of ethnic relationship’, Am. J. Hum. Genet., 1953, 5, (4), p. 389.
  32. 32)
      • 31. Maltoni, D., Maio, D., Jain, A.K., et al: ‘Handbook of fingerprint recognition’ (Springer, UK, 2009).
  33. 33)
      • 127. He, S., Zhang, C., Hao, P.: ‘Comparative study of features for fingerprint indexing’. Int. Conf. on Image Processing (ICIP), Cairo, Egypt, 2009, pp. 2749–2752.
  34. 34)
      • 13. European Union: ‘Regulation (EU) 2017/2226 of the European Parliament and of the Council’, Official Journal of the European Union, 2017. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32017R2226, accessed 11 6 2019.
  35. 35)
      • 156. Drozdowski, P., Rathgeb, C., Hofbauer, H., et al: ‘Towards pre-alignment of near-infrared iris images’. Int. Joint Conf. on Biometrics (IJCB), Denver, USA, 2017, pp. 359–366.
  36. 36)
      • 135. Jayaraman, U., Gupta, P.: ‘Iris code hashing’. Int. Conf. on Communications (ICC), Budapest, Hungary, 2013, pp. 2123–2127.
  37. 37)
      • 110. Mansukhani, P., Tulyakov, S., Govindaraju, V.: ‘A framework for efficient fingerprint identification using a minutiae tree’, Syst. J., 2010, 4, (2), pp. 126–137.
  38. 38)
      • 99. Galton, F.: ‘Fingerprint directories’ (Macmillan and Company, UK, 1895).
  39. 39)
      • 5. Unique Identification Authority of India: ‘Aadhaar dashboard’, UIDAI, 2018. Available at: https://www.uidai.gov.in/aadhaar_dashboard/, accessed 11 6 2019.
  40. 40)
      • 71. Surbiryala, J., Raghavendra, R., Busch, C.: ‘Finger vein indexing based on binary features’. Colour and Visual Computing Symp. (CVCS), Gjøvik, Norway, 2015, pp. 1–6.
  41. 41)
      • 60. Gentile, J.E., Ratha, N., Connell, J.: ‘An efficient, two-stage iris recognition system’. Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington D.C., USA, 2009, pp. 211–215.
  42. 42)
      • 178. Lastra, M., Gutiérrez, P.D., Benítez, J.M., et al: ‘GPU processing for biometric big data based identification. Why and what for?’, Biostat Biometrics Open Access J., 2017, 2, pp. 1–4.
  43. 43)
      • 54. Konrad, M., Stögner, H., Uhl, A., et al: ‘Computationally efficient serial combination of rotation-invariant and rotation compensating iris recognition algorithms’. Int. Conf. on Computer Vision Theory and Applications (VISAPP), Angers, France, 2010, vol. 1, pp. 85–90.
  44. 44)
      • 118. Drozdowski, P., Rathgeb, C., Busch, C.: ‘Bloom filter-based search structures for indexing and retrieving iris-codes’, IET Biometrics, 2018, 7, pp. 260–268.
  45. 45)
      • 207. Cappelli, R., Ferrara, M., Maltoni, D.: ‘FIDXICB-2013’, University of Bologna Biometric System Laboratory, 2013. Available at: https://biolab.csr.unibo.it/fvcongoing/UI/Form/ICB2013FIDX.aspx, accessed 11 6 2019.
  46. 46)
      • 141. Wolfson, H.J., Rigoutsos, I.: ‘Geometric hashing: an overview’, Comput. Sci. Eng., 1997, 4, (4), pp. 10–21.
  47. 47)
      • 83. Germain, R.S., Califano, A., Colville, S.: ‘Fingerprint matching using transformation parameter clustering’, Comput. Sci. Eng., 1997, 4, (4), pp. 42–49.
  48. 48)
      • 76. Drozdowski, P., Fischer, D., Rathgeb, C., et al: ‘Database binning and retrieval in multi-fingerprint identification systems’. Int. Workshop on Information Forensics and Security (WIFS), Hong Kong, 2018, pp. 1–7.
  49. 49)
      • 160. Singh, D., Reddy, C.K.: ‘A survey on platforms for big data analytics’, J. Big. Data., 2015, 2, (1), pp. 1–20.
  50. 50)
      • 28. Rachkovskij, D.A.: ‘Index structures for fast similarity search for real vectors II’, Cybern. Syst. Anal., 2018, 54, (2), pp. 320–335.
  51. 51)
      • 174. Bouraoui, H., Jerad, C., Chattopadhyay, A., et al: ‘Hardware architectures for embedded speaker recognition applications: a survey’, Trans. Embedded Comput. Syst., 2017, 16, (3), p. 78.
  52. 52)
      • 103. Qiu, X., Sun, Z., Tan, T.: ‘Global texture analysis of iris images for ethnic classification’, Adv. Biometrics, 2005, 3832, pp. 411–418.
  53. 53)
      • 181. Schroff, F., Kalenichenko, D., Philbin, J.: ‘Facenet: a unified embedding for face recognition and clustering’. Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, USA, 2015, pp. 815–823.
  54. 54)
      • 106. Dantcheva, A., Erdogmus, N., Dugelay, J.L.: ‘On the reliability of eye color as a soft biometric trait’. Workshop on Applications of Computer Vision (WACV), Hawaii, USA, 2011, pp. 227–231.
  55. 55)
      • 66. Yi, D., Lei, Z., Hu, Y., et al: ‘Fast matching by 2 lines of code for large scale face recognition systems’, arXiv preprint arXiv:13027180, 2013.
  56. 56)
      • 1. Thakkar, D.: ‘Global biometric market analysis: trends and future prospects’, Bayometric, 2018. Available at: https://www.bayometric.com/global-biometric-market-analysis/, accessed 11 6 2019.
  57. 57)
      • 19. Daugman, J.: ‘History of iris recognition’, University of Cambridge – Computer Laboratory, 2019. Available at: https://www.cl.cam.ac.uk/jgd1000/history.html, accessed 11 6 2019.
  58. 58)
      • 4. Unique Identification Authority of India: ‘Role of biometric technology in Aadhaar enrollment’ (UIDAI, India, 2012).
  59. 59)
      • 85. Liu, M., Jiang, X., Kot, A.C.: ‘Efficient fingerprint search based on database clustering’, Pattern Recognit., 2007, 40, (6), pp. 1793–1803.
  60. 60)
      • 168. Chen, A.T.J., Biglari-Abhari, M., Wang, K.I.K., et al: ‘Convolutional neural network acceleration with hardware/software codesign’, Appl. Intell., 2018, 48, (5), pp. 1288–1301.
  61. 61)
      • 93. Sun, Z., Zhang, H., Tan, T., et al: ‘Iris image classification based on hierarchical visual codebook’, Trans. Pattern Anal. Mach. Intell., 2014, 36, (6), pp. 1120–1133.
  62. 62)
      • 2. Bhutani, A., Bhardwaj, P.: ‘Biometrics market size by application’. GMI493 Global Market Insights, 2017.
  63. 63)
      • 21. Chávez, E., Navarro, G., Baeza-Yates, R., et al: ‘Searching in metric spaces’, Comput. Surv., 2001, 33, (3), pp. 273–321.
  64. 64)
      • 52. Wang, D., Jain, A.K.: ‘Face retriever: pre-filtering the gallery via deep neural net’. Int. Conf. on Biometrics (ICB), Phuket, Thailand, 2015, pp. 473–480.
  65. 65)
      • 185. Lin, Y., Du, E.Y., Zhou, Z., et al: ‘An efficient parallel approach for sclera vein recognition’, Trans. Inf. Forensics Secur., 2014, 9, (2), pp. 147–157.
  66. 66)
      • 78. Yu, L., Zhang, D., Wang, K., et al: ‘Coarse iris classification using box-counting to estimate fractal dimensions’, Pattern Recognit., 2005, 38, (11), pp. 1791–1798.
  67. 67)
      • 193. Li, G., Yang, B., Busch, C.: ‘A fingerprint indexing algorithm on encrypted domain’. Trustcom/BigDataSE/ISPA, Tianjin, China, 2016, pp. 1030–1037.
  68. 68)
      • 139. Cormen, T.H., Leiserson, C.E., Rivest, R.L., et al: ‘Introduction to algorithms’ (MIT press, USA, 2009).
  69. 69)
      • 148. Xu, H., Veldhuis, R.N.J., Kevenaar, T.A.M., et al: ‘Spectral minutiae: a fixed-length representation of a minutiae set’. Conf. on Computer Vision and Pattern RecognitionWorkshops (CVPRW), Alaska, USA, 2008, pp. 1–6.
  70. 70)
      • 56. Dey, S., Samanta, D.: ‘Iris data indexing method using Gabor energy features’, Trans. Inf. Forensics Secur., 2012, 7, (4), pp. 1192–1203.
  71. 71)
      • 162. López, M., Daugman, J., Cantó, E.: ‘Hardware–software co-design of an iris recognition algorithm’, IET Inf. Sec., 2011, 5, (1), pp. 60–68.
  72. 72)
      • 114. Jayaraman, U., Prakash, S., Gupta, P.: ‘An efficient color and texture based iris image retrieval technique’, Expert Syst. Appl., 2012, 39, (5), pp. 4915–4926.
  73. 73)
      • 164. Sirowy, S., Forin, A.: ‘Where's the beef? why FPGAs are so fast’. MSR-TR-2008-130, Microsoft Research, 2008.
  74. 74)
      • 65. Chen, F., Huang, X., Zhou, J.: ‘Hierarchical minutiae matching for fingerprint and palmprint identification’, Trans. Image Process., 2013, 22, (12), pp. 4964–4971.
  75. 75)
      • 204. Jain, A., Klare, B., Ross, A.: ‘Guidelines for best practices in biometrics research’. Int. Conf. on Biometrics (ICB), Phuket, Thailand, 2015, pp. 541–545.
  76. 76)
      • 84. Ross, A., Mukherjee, R.: ‘Augmenting ridge curves with minutiae triplets for fingerprint indexing’. Biometric Technology for Human Identification IV, Orlando, USA, 2007, vol. 6539, pp. 1–12.
  77. 77)
      • 208. National Institute of Standards and Technology: ‘Face recognition vendor test (FRVT) 1:N evaluation’, NIST, 2017. Available at: https://www.nist.gov/programs-projects/facerecognition-vendor-test-frvt-1n-2018-evaluation, accessed 11 6 2019.
  78. 78)
      • 125. Gupta, P., Sana, A., Mehrotra, H., et al: ‘An efficient indexing scheme for binary feature based biometric database’. Biometric Technology for Human Identification IV, Orlando, USA, 2007, vol. 6539, pp. 1–10.
  79. 79)
      • 192. Rathgeb, C., Uhl, A.: ‘A survey on biometric cryptosystems and cancelable biometrics’, EURASIP J. Inf. Secur., 2011, 2011, pp. 3–28.
  80. 80)
      • 98. Dantcheva, A., Elia, P., Ross, A.: ‘What else does your biometric data reveal? A survey on soft biometrics’, Trans. Inf. Forensics Secur., 2016, 11, (3), pp. 441–467.
  81. 81)
      • 33. Bowyer, K.W., Burge, M.J.: ‘Handbook of iris recognition’ (Springer, UK, 2016).
  82. 82)
      • 36. Schuch, P.: ‘Survey on features for fingerprint indexing’, IET Biometrics, 2019, 8, (1), pp. 1–13.
  83. 83)
      • 172. Jadhav, M., Nerkar, P.M.: ‘Implementation of an embedded hardware of FVRS on FPGA’. Int. Conf. on Information Processing (ICIP), Québec City, Canada, 2015, pp. 48–53.
  84. 84)
      • 144. Drozdowski, P., Struck, F., Rathgeb, C., et al: ‘Benchmarking binarisation schemes for deep face templates’. Int. Conf. on Image Processing (ICIP), Athens, Greece, 2018, pp. 191–195.
  85. 85)
      • 199. Daugman, J.: ‘How iris recognition works’, Trans. Circuits Syst. Video Technol., 2004, 14, (1), pp. 21–30.
  86. 86)
      • 51. Chen, B.C., Chen, Y.Y., Kuo, Y.H., et al: ‘Scalable face image retrieval using attribute-enhanced sparse codewords’, Trans. Multimed., 2013, 15, (5), pp. 1163–1173.
  87. 87)
      • 48. Gyaourova, A., Ross, A.: ‘A coding scheme for indexing multimodal biometric databases’. Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), Miami, USA, 2009, pp. 93–98.
  88. 88)
      • 8. Gemalto: ‘DHS's automated biometric identification system IDENT – the heart of biometric visitor identification in the USA’, Gemalto Case Studies, 2019. Available at: https://www.gemalto.com/govt/customercases/ident-automated-biometric-identification-system, accessed 11 6 2019.
  89. 89)
      • 203. Grother, P., Ngan, M., Hanaoka, K.: ‘Ongoing face recognition vendor test (FRVT) part 2: identification’. NISTIR 8238, National Institute of Standards and Technology, 2018.
  90. 90)
      • 55. Gadde, R.B., Adjeroh, D., Ross, A.: ‘Indexing iris images using the burrows-wheeler transform’. Int. Workshop on Information Forensics and Security (WIFS), Seattle, USA, 2010, pp. 1–6.
  91. 91)
      • 170. Fons, M., Fons, F., Cantó, E.: ‘Fingerprint image processing acceleration through run-time reconfigurable hardware’, Trans. Circuits Syst. II, Express Briefs, 2010, 57, (12), pp. 991–995.
  92. 92)
      • 43. Liang, X., Bishnu, A., Asano, T.: ‘A robust fingerprint indexing scheme using minutia neighborhood structure and low-order Delaunay triangles’, Trans. Inf. Forensics Secur., 2007, 2, (4), pp. 721–733.
  93. 93)
      • 6. European Union: ‘Regulation (EU) no 603/2013 of the European Parliament and of the Council’, Off. J. Eur. Union, 2013. Available at: https://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=celex:32013R0603, accessed 11 6 2019, 56, pp. 1–30.
  94. 94)
      • 115. Barbu, T., Luca, M.: ‘Content-based iris indexing and retrieval model using spatial acces methods’. Int. Symp. on Signals, Circuits and Systems (ISSCS), Iasi, Romania, 2015, pp. 1–4.
  95. 95)
      • 39. de Boer, J., Bazen, A.M., Gerez, S.H.: ‘Indexing fingerprint databases based on multiple features’. Annual Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands, 2001, pp. 300–306.
  96. 96)
      • 134. Rathgeb, C., Uhl, A.: ‘Iris-biometric hash generation for biometric database indexing’. Int. Conf. on Pattern Recognition (ICPR), Istanbul, Turkey, 2010, pp. 2848–2851.
  97. 97)
      • 200. Gorodnichy, D.O., Chumakov, M.P.: ‘Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset’, IET Biometrics, 2019, 8, (1), pp. 29–39.
  98. 98)
      • 86. Biswas, S., Ratha, N.K., Aggarwal, G., et al: ‘Exploring ridge curvature for fingerprint indexing’. Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington D.C., USA, 2008, pp. 1–6.
  99. 99)
      • 166. Tessier, R., Pocek, K., DeHon, A.: ‘Reconfigurable computing architectures’, Proc. IEEE, 2015, 103, (3), pp. 332–354.
  100. 100)
       • 10. Consortium for Elections and Political Process Strengthening: ‘Assessment of electoral preparations in the Democratic Republic of the Congo’, CEPPS, 2018.
  101. 101)
       • 14. European Commission: ‘Smart borders’, EU Migration and Home Affairs, 2018. Available at: https://ec.europa.eu/homeaffairs/what-we-do/policies/borders-and-visas/smartborders_en, accessed 11 6 2019.
  102. 102)
       • 201. Komarinski, P.: ‘Automated fingerprint identification systems (AFIS)’ (Elsevier, USA, 2005).
  103. 103)
       • 68. Ross, A., Sunder, M.S.: ‘Block based texture analysis for iris classification and matching’. Conf. on Computer Vision and Pattern Recognition – Workshops (CVPRW), San Francisco, USA, 2010, pp. 30–37.
  104. 104)
       • 105. Singh, M., Nagpal, S., Vatsa, M., et al: ‘Gender and ethnicity classification of iris images using deep class-encoder’. Int. Joint Conf. on Biometrics (IJCB), Denver, USA, 2017, pp. 666–673.
  105. 105)
       • 159. Berten Digital Signal Processing: ‘GPU vs FPGA performance comparison’. BWP001 v1.0, Berten DSP, 2016.
  106. 106)
       • 41. Feng, J., Cai, A.: ‘Fingerprint indexing using ridge invariants’. Int. Conf. on Pattern Recognition (ICPR), Hong Kong, 2006, vol. 4, pp. 433–436.
  107. 107)
       • 94. Nalla, P.R., Chalavadi, K.M.: ‘Iris classification based on sparse representations using on-line dictionary learning for large-scale de-duplication applications’, SpringerPlus, 2015, 4, (1), pp. 238–248.
  108. 108)
       • 111. Dewangan, J., Dey, S., Samanta, D.: ‘Face images database indexing for person identification problem’, Int. J. Biometrics Bioinf., 2013, 7, (2), pp. 93–122.
  109. 109)
       • 197. Al-Raisi, A.N., Al-Khouri, A.M.: ‘Iris recognition and the challenge of homeland and border control security in UAE’, Telemat. Inform., 2008, 25, (2), pp. 117–132.
  110. 110)
       • 158. Singh, M., Singh, R., Ross, A.: ‘A comprehensive overview of biometric fusion’, Inf. Fusion, 2019, 52, pp. 187–205.
  111. 111)
       • 3. Markets and Markets: ‘Biometric system market by authentication type – global forecast to 2023’. SE 3449, Markets and Markets, 2018.
  112. 112)
       • 132. Kaushik, V.D., Umarani, J., Gupta, A.K., et al: ‘An efficient indexing scheme for face database using modified geometric hashing’, Neurocomputing, 2013, 116, pp. 208–221.
  113. 113)
       • 12. Department of Homeland Security: ‘DHS/ALL-041 external biometric records (EBR) system of records’, Federal Register, 2018. Available at: https://www.regulations.gov/docket?D=DHS-2017-0039, accessed 11 6 2019.
  114. 114)
       • 126. Shuai, X., Zhang, C., Hao, P.: ‘Fingerprint indexing based on composite set of reduced SIFT features’. Int. Conf. on Pattern Recognition (ICPR), Tampa, USA, 2008, pp. 1–4.
  115. 115)
       • 195. Wang, Y., Wan, J., Guo, J., et al: ‘Inference-based similarity search in randomized Montgomery domains for privacy-preserving biometric identification’, Trans. Pattern Anal. Mach. Intell., 2018, 40, (7), pp. 1611–1624.
  116. 116)
       • 140. Lamdan, Y., Wolfson, H.J.: ‘Geometric hashing: a general and efficient model-based recognition scheme’. Int. Conf. on Computer Vision (ICCV), Tampa, USA, 1988, pp. 238–249.
  117. 117)
       • 50. Mohanty, P., Sarkar, S., Kasturi, R., et al: ‘Subspace approximation of face recognition algorithms: an empirical study’, Trans. Inf. Forensics Secur., 2008, 3, (4), pp. 734–748.
  118. 118)
       • 128. Cappelli, R., Ferrara, M., Maltoni, D.: ‘Fingerprint indexing based on minutia cylinder-code’, Trans. Pattern Anal. Mach. Intell., 2011, 33, (5), pp. 1051–1057.
  119. 119)
       • 183. Ghafoor, M., Iqbal, S., Tariq, S.A., et al: ‘Efficient fingerprint matching using GPU’, IET Image Process., 2018, 12, (2), pp. 274–284.
  120. 120)
       • 53. Wang, D., Otto, C., Jain, A.K.: ‘Face search at scale’, Trans. Pattern Anal. Mach. Intell., 2017, 39, (6), pp. 1122–1136.
  121. 121)
       • 189. Nandakumar, K., Jain, A.K.: ‘Biometric template protection: bridging the performance gap between theory and practice’, Signal Process. Mag., 2015, 32, (5), pp. 88–100.
  122. 122)
       • 188. Federal Bureau of Investigation: ‘The science of fingerprints: classification and uses’ (General Press, USA, 2013).
  123. 123)
       • 24. Wang, J., Shen, H.T., Song, J., et al: ‘Hashing for similarity search: a survey’, arXiv preprint arXiv:14082927, 2014, pp. 1–29.
  124. 124)
       • 176. Stone, J.E., Gohara, D., Shi, G.: ‘OpenCL: a parallel programming standard for heterogeneous computing systems’, Comput. Sci. Eng., 2010, 12, (3), pp. 66–73.
  125. 125)
       • 77. Park, U., Liao, S., Klare, B., et al: ‘Face finder: filtering a large face database using scars, marks and tattoos’. MSU-CSE-11-15, Michigan State University, 2011.
  126. 126)
       • 182. Vandal, N.A., Savvides, M.: ‘CUDA accelerated iris template matching on graphics processing units (GPUs)’. Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington D.C., USA, 2010, pp. 1–7.
  127. 127)
       • 91. Klare, B.F., Blanton, A., Klein, B.: ‘Efficient face retrieval using synecdoches’. Int. Joint Conf. on Biometrics (IJCB), Clearwater, USA, 2014, pp. 1–7.
  128. 128)
       • 117. Drozdowski, P., Rathgeb, C., Busch, C.: ‘Multi-iris indexing and retrieval: fusion strategies for Bloom filter-based search structures’. Int. Joint Conf. on Biometrics (IJCB), Denver, USA, 2017, pp. 46–53.
  129. 129)
       • 152. Lim, M.H., Teoh, A.B.J., Kim, J.: ‘Biometric feature-type transformation: making templates compatible for secret protection’, IEEE Signal Process. Mag., 2015, 32, (5), pp. 77–87.
  130. 130)
       • 74. Hollingsworth, K.P., Bowyer, K.W., Flynn, P.J.: ‘The best bits in an iris code’, Trans. Pattern Anal. Mach. Intell., 2009, 31, (6), pp. 964–973.
  131. 131)
       • 40. Bhanu, B., Tan, X.: ‘Fingerprint indexing based on novel features of minutiae triplets’, Trans. Pattern Anal. Mach. Intell., 2003, 25, (5), pp. 616–622.
  132. 132)
       • 130. Wang, Y., Wang, L., Cheung, Y.M., et al: ‘Learning compact binary codes for hash-based fingerprint indexing’, Trans. Inf. Forensics Secur., 2015, 10, (8), pp. 1603–1616.
  133. 133)
       • 198. Daugman, J., Malhas, I.: ‘Iris recognition border-crossing system in the UAE’, Int. Airpt. Rev., 2004, 8, (2), pp. 1–5.
  134. 134)
       • 20. Gionis, A., Indyk, P., Motwani, R.: ‘Similarity search in high dimensions via hashing’. Int. Conf. on Very Large Data Bases (VLDB), Morgan Kaufmann, 1999, pp. 518–529.
  135. 135)
       • 143. Schlett, T., Rathgeb, C., Busch, C.: ‘A binarization scheme for face recognition based on multi-scale block local binary patterns’. Int. Conf. of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 2016, pp. 1–4.
  136. 136)
       • 133. Mehrotra, H., Majhi, B., Gupta, P.: ‘Robust iris indexing scheme using geometric hashing of sift keypoints’, J. Netw. Comput. Appl., 2010, 33, (3), pp. 300–313.
  137. 137)
       • 63. Pflug, A., Rathgeb, C., Scherhag, U., et al: ‘Binarization of spectral histogram models: an application to efficient biometric identification’. Int. Conf. on Cybernetics (CYBCONF), Gdynia, Poland, 2015, pp. 501–506.
  138. 138)
       • 154. Cappelli, R., Ferrara, M., Maio, D.: ‘Candidate list reduction based on the analysis of fingerprint indexing scores’, Trans. Inf. Forensics Secur., 2011, 6, (3), pp. 1160–1164.
  139. 139)
       • 123. Wang, K., Yang, L., Su, K., et al: ‘Binary search path of vocabulary tree based finger vein image retrieval’. Int. Conf. on Biometrics (ICB), Halmstad, Sweden, 2016, pp. 1–8.
  140. 140)
       • 175. Nickolls, J., Buck, I., Garland, M., et al: ‘Scalable parallel programming with CUDA’. SIGGRAPH Classes, Los Angeles, USA, 2008, pp. 42–53.
  141. 141)
       • 179. Barrus, J.: ‘Cloud TPU machine learning accelerators now available in beta’, Google, 2018. Available at: https://cloud.google.com/blog/products/gcp/cloud-tpu-machine-learningaccelerators-now-available-in-beta, accessed 11 6 2019.
  142. 142)
       • 27. Rachkovskij, D.A.: ‘Index structures for fast similarity search for real-valued vectors I’, Cybern. Syst. Anal., 2018, 54, (1), pp. 152–164.
  143. 143)
       • 79. Puhan, N.B., Sudha, N.: ‘A novel iris database indexing method using the iris color’. Conf. on Industrial Electronics and Applications, Singapore, 2008, pp. 1886–1891.
  144. 144)
       • 59. Wu, Z., Ke, Q., Sun, J., et al: ‘Scalable face image retrieval with identity-based quantization and multi-reference re-ranking’. Conf. on. Computer Vision and Pattern Recognition (CVPR), San Francisco, USA, 2010, pp. 3469–3476.
  145. 145)
       • 171. Stekas, N., van den Heuvel, D.: ‘Face recognition using local binary patterns histograms (LBPH) on an FPGA-based system on chip (SoC)’. Int. Parallel and Distributed Processing Symp. Workshops (IPDPSW), Chicago, USA, 2016, pp. 300–304.
  146. 146)
       • 49. Gyaourova, A., Ross, A.: ‘Index codes for multibiometric pattern retrieval’, Trans. Inf. Forensics Secur., 2012, 7, (2), pp. 518–529.
  147. 147)
       • 57. Kavati, I., Prasad, M.V.N.K., Bhagvati, C.: ‘Vein pattern indexing using texture and hierarchical decomposition of Delaunay triangulation’. Int. Symp. on Security in Computing and Communication, Mysore, India, 2013, pp. 213–222.
  148. 148)
       • 191. ISO/IEC JTC1 SC27 IT Security techniques: ‘ISO/IEC 24745:2011. Information technology – security techniques – biometric information protection’ (International Organization for Standardization, 2011).
  149. 149)
       • 70. Rathgeb, C., Uhl, A., Wild, P.: ‘Incremental iris recognition: a single-algorithm serial fusion strategy to optimize time complexity’. Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington D.C., USA, 2010.
  150. 150)
       • 122. Jayaraman, U., Prakash, S., Gupta, P.: ‘Indexing multimodal biometric databases using kd-tree with feature level fusion’. Int. Conf. on Information Systems Security, Ystad, Sweden, 2008, pp. 221–234.
  151. 151)
       • 72. Henry, E.R.: ‘Classification and uses of finger prints’ (HM Stationery Office, UK, 1900).
  152. 152)
       • 137. Badrinath, G.S., Gupta, P., Mehrotra, H.: ‘Score level fusion of voting strategy of geometric hashing and SURF for an efficient palmprint-based identification’, J. Real-Time Image Process., 2013, 8, (3), pp. 265–284.
  153. 153)
       • 161. Rathgeb, C., Buchmann, N., Hofbauer, H., et al: ‘Methods for accuracy-preserving acceleration of large-scale comparisons in CPU-based iris ecognition systems’, IET Biometrics, 2018, 7, (4), pp. 356–364.
  154. 154)
       • 102. Wayman, J.L.: ‘Multifinger penetration rate and ROC variability for automatic fingerprint identification systems’, in Ratha, N., Bolle, R. (Eds.): ‘Automatic fingerprint recognition systems’ (Springer, USA, 2004), pp. 305–316.
  155. 155)
       • 9. Federal Bureau of Investigation: ‘CODIS – NDIS statistics’, FBI, 2018. Available at: https://www.fbi.gov/services/laboratory/biometric-analysis/codis/ndis-statistics, accessed 11 6 2019.
  156. 156)
       • 205. Murphy, T.M., Broussard, R., Rakvic, R., et al: ‘Use of synthetic data to test biometric algorithms’, J. Electron. Imaging, 2016, 25, (4), p. 043023.
  157. 157)
       • 147. Jain, A.K., Prabhakar, S., Hong, L., et al: ‘Filterbank-based fingerprint matching’, Trans. Image Process., 2000, 9, (5), pp. 846–859.
  158. 158)
       • 119. Damer, N., Terhörst, P., Braun, A., et al: ‘Indexing of single and multiinstance iris data based on LSH-forest and rotation invariant representation’. Int. Conf. on Computer Analysis of Images and Patterns (CAIP), Ystad, Sweden, 2017, pp. 190–201.
  159. 159)
       • 121. Proença, H.: ‘Iris biometrics: indexing and retrieving heavily degraded data’, Trans. Inf. Forensics Secur., 2013, 8, (12), pp. 1975–1985.
  160. 160)
       • 113. Khalaf, E.T., Mohammed, M., Moorthy, K.: ‘Robust partitioning and indexing for iris biometric database based on local features’, IET Biometrics, 2018, 7, (6), pp. 589–597.
  161. 161)
       • 184. Yi, S., Yoon, I., Oh, C., et al: ‘Real-time integrated face detection and recognition on embedded GPGPUs’. Symp. on Embedded Systems for Real-time Multimedia (ESTIMedia), Montreal, Canada, 2014, pp. 98–107.
  162. 162)
       • 81. Palla, S., Chikkerur, S., Govindaraju, V., et al: ‘Classification and indexing in large biometric databases’. Biometrics Consortium Conf., New York, USA, 2004, pp. 1–3.
  163. 163)
       • 129. Yuan, B., Su, F., Cai, A.: ‘Fingerprint retrieval approach based on novel minutiae triplet features’. Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington D.C., USA, 2012, pp. 170–175.
  164. 164)
       • 23. Zezula, P., Amato, G., Dohnal, V., et al: ‘Similarity search: the metric space approach’ (Springer, USA, 2006).
  165. 165)
       • 145. Gentile, J.E., Ratha, N., Connell, J.: ‘SLIC: short-length iris codes’. Int. Conf. on Biometrics: Theory, Applications, and Systems (BTAS), Washington D.C., USA, 2009, pp. 1–5.
  166. 166)
       • 45. Feng, J., Jain, A.K.: ‘Filtering large fingerprint database for latent matching’. Int. Conf. on Pattern Recognition (ICPR), Tampa, USA, 2008, pp. 1–4.
  167. 167)
       • 44. Wang, Y., Hu, J., Phillips, D.: ‘A fingerprint orientation model based on 2D Fourier expansion (FOMFE) and its application to singular-point detection and fingerprint indexing’, Trans. Pattern Anal. Mach. Intell., 2007, 29, (4), pp. 573–585.
  168. 168)
       • 18. Institute of Electrical and Electronics Engineers: ‘Biometric council newsletter’, IEEE, 2015. Available at: http://ieee-biometrics.org/images/pdf/Newsletter_Nov_2015_corrected.pdf, accessed 11 6 2019.
  169. 169)
       • 26. Rachkovskij, D.A.: ‘Index structures for fast similarity search for binary vectors’, Cybern. Syst. Anal., 2017, 53, (5), pp. 799–820.
  170. 170)
       • 58. You, J., Kong, W.K., Zhang, D., et al: ‘On hierarchical palmprint coding with multiple features for personal identification in large databases’, Trans. Circuits Syst. Video Technol., 2004, 14, (2), pp. 234–243.
  171. 171)
       • 46. Cappelli, R.: ‘Fast and accurate fingerprint indexing based on ridge orientation and frequency’, Trans. Syst. Man Cybern. B, Cybern., 2011, 41, (6), pp. 1511–1521.
  172. 172)
       • 11. Paynter, T.: ‘Northrop Grumman wins $95 million award from department of homeland security to develop next-generation biometric identification services system’, Northrop Grumman, 2018. Available at: https://news.northropgrumman.com/news/releases/northropgrumman-wins-95-million-award-from-departmentof-homeland-security-to-develop-next-generationbiometric-identification-services-system, accessed 11 6 2019.
  173. 173)
       • 155. Rathgeb, C., Hofbauer, H., Uhl, A., et al: ‘TripleA: accelerated accuracy preserving alignment for iris-codes’. Int. Conf. on Biometrics (ICB), Halmstad, Sweden, 2016, pp. 1–8.
  174. 174)
       • 187. Moses, K.R., Higgins, P., McCabe, M., et al: ‘Automated fingerprint identification system (AFIS)’, in McRoberts, A. (Ed.): ‘Fingerprint sourcebook’ (US Department of Justice, USA, 2010), pp. 1–33.
  175. 175)
       • 136. Panda, A.K., Mehrotra, H., Majhi, B.: ‘Parallel geometric hashing for robust iris indexing’, J. Real-Time Image Process., 2013, 8, (3), pp. 341–349.
  176. 176)
       • 206. Marcel, S.: ‘BEAT – biometrics evaluation and testing’, Biometric Technol. Today, 2013, 2013, (1), pp. 5–7.
  177. 177)
       • 75. Zheng, R., Zhang, C., He, S., et al: ‘A novel composite framework for large-scale fingerprint database indexing and fast retrieval’. Int. Conf. on Hand-Based Biometrics (ICHB), Hong Kong, 2011, pp. 1–6.
  178. 178)
       • 202. Gemalto: ‘Automated fingerprint identification system (AFIS) – a short history’, Gemalto Case Studies, 2019. Available at: https://www.gemalto.com/govt/biometrics/afis-history, accessed 11 6 2019.
  179. 179)
       • 194. Drozdowski, P., Garg, S., Rathgeb, C., et al: ‘Privacy-preserving indexing of Iris-codes with cancelable Bloom filter-based search structures’. European Signal Processing Conf. (EUSIPCO), Rome, Italy, 2018, pp. 2360–2364.
  180. 180)
       • 149. Yang, J.C., Park, D.S.: ‘A fingerprint verification algorithm using tessellated invariant moment features’, Neurocomputing, 2008, 71, (10–12), pp. 1939–1946.
  181. 181)
       • 37. Liu, L., Chen, J., Fieguth, P., et al: ‘From BoW to CNN: two decades of texture representation for texture classification’, Int. J. Comput. Vis., 2019, 127, (1), pp. 74–109.
  182. 182)
       • 173. Koukounis, D., Ttofis, C., Papadopoulos, A., et al: ‘A high performance hardware architecture for portable, low-power retinal vessel segmentation’, Integr. VLSI J., 2014, 47, (3), pp. 377–386.
  183. 183)
       • 22. Hjaltason, G.R., Samet, H.: ‘Index-driven similarity search in metric spaces (survey article)’, Trans. Database Syst., 2003, 28, (4), pp. 517–580.
  184. 184)
       • 107. Wu, X., Kumar, V., Quinlan, J.R., et al: ‘Top 10 algorithms in data mining’, Knowl. Inf. Syst., 2008, 14, (1), pp. 1–37.
  185. 185)
       • 42. Li, J., Yau, W.Y., Wang, H.: ‘Fingerprint indexing based on symmetrical measurement’. Int. Conf. on Pattern Recognition (ICPR), Hong Kong, 2006, vol. 1, pp. 1038–1041.
  186. 186)
       • 17. Daugman, J.: ‘Biometric decision landscapes’. UCAM-CL-TR-482, University of Cambridge - Computer Laboratory, 2000.
  187. 187)
       • 61. Tang, D., Huang, B., Li, R., et al: ‘A person retrieval solution using finger vein patterns’. Int. Conf. on Pattern Recognition (ICPR), Istanbul, Turkey, 2010, pp. 1306–1309.
  188. 188)
       • 190. European Parliament: ‘Regulation (EU) 2016/679’, Off. J. Eur. Union, 2016, L119, pp. 1–88.
  189. 189)
       • 146. Rathgeb, C., Uhl, A., Wild, P.: ‘On combining selective best bits of iris-codes’. European Workshop on Biometrics and Identity Management (BioID), Brandenburg, Germany, 2011, pp. 227–237.
  190. 190)
       • 69. Hämmerle-Uhl, J., Penn, G., Pötzelsberger, G., et al: ‘Size-reduction strategies for iris codes’, Int. J. Comput. Electr. Autom. Control Inf. Eng., 2015, 9, (1), pp. 290–293.
  191. 191)
       • 142. Cappelli, R., Ferrara, M., Maltoni, D.: ‘Minutia cylinder-code: a new representation and matching technique for fingerprint recognition’, Trans. Pattern Anal. Mach. Intell., 2010, 32, (12), pp. 2128–2141.
  192. 192)
       • 157. Drozdowski, P., Rathgeb, C., Busch, C.: ‘Turning a vulnerability into an asset: accelerating facial identification with morphing’. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 2582–2586.
  193. 193)
       • 120. Damer, N., Terhörst, P., Braun, A., et al: ‘General Borda count for multibiometric retrieval’. Int. Joint Conf. on Biometrics (IJCB), Denver, USA, 2017, pp. 420–428.
  194. 194)
       • 104. Tapia, J.E., Perez, C.A., Bowyer, K.W.: ‘Gender classification from iris images using fusion of uniform local binary patterns’. European Conf. on Computer Vision (ECCV), Zurich, Switzerland, 2014, pp. 751–763.
  195. 195)
       • 80. Zhao, Q.: ‘A new approach for noisy iris database indexing based on color information’. Int. Conf. on Computer Science Education (CSE), Qingdao, China, 2011, pp. 28–31.
  196. 196)
       • 67. Hao, F., Daugman, J., Zielinski, P.: ‘A fast search algorithm for a large fuzzy database’, Trans. Inf. Forensics Secur., 2008, 3, (2), pp. 203–212.
  197. 197)
       • 30. Jain, A.K., Flynn, P., Ross, A.: ‘Handbook of biometrics’ (Springer, USA, 2007).
  198. 198)
       • 89. Perronnin, F., Dugelay, J.L.: ‘Clustering face images with application to image retrieval in large databases’. Biometric Technology for Human Identification II, Orlando, USA, 2005, vol. 5779, pp. 256–265.
  199. 199)
       • 97. Pflug, A., Busch, C., Ross, A.: ‘2D ear classification based on unsupervised clustering’. Int. Joint Conf. on Biometrics (IJCB), Clearwater, USA, 2014, pp. 1–8.
  200. 200)
       • 124. Mhatre, A., Chikkerur, S., Govindaraju, V.: ‘Indexing biometric databases using pyramid technique’. Int. Conf. on Audio-and Video-Based Biometric Person Authentication, New York, USA, 2005, pp. 841–849.
  201. 201)
       • 108. Jain, A.K., Murty, M.N., Flynn, P.J.: ‘Data clustering: a review’, Comput. Surv., 1999, 31, (3), pp. 264–323.
  202. 202)
       • 153. Kuehlkamp, A., Bowyer, K.: ‘Found a good match: should i keep searching? – accuracy and performance in iris matching using 1-to-first search’, Image Vis. Comput., 2018, 73, pp. 17–27.
  203. 203)
       • 101. Galar, M., Derrac, J., Peralta, D., et al: ‘A survey of fingerprint classification part II: experimental analysis and ensemble proposal’, Knowl.-Based Syst., 2015, 81, pp. 98–116.
  204. 204)
       • 16. ISO/IEC JTC1 SC37 Biometrics: ‘ISO/IEC 19795-1:2006. Information technology – biometric performance testing and reporting – part 1: principles and framework’ (International Organization for Standardization, 2006).
  205. 205)
       • 88. Iloanusi, O.N.: ‘Fusion of finger types for fingerprint indexing using minutiae quadruplets’, Pattern Recognit. Lett., 2014, 38, pp. 8–14.
  206. 206)
       • 131. Li, G., Yang, B., Busch, C.: ‘A fingerprint indexing scheme with robustness against sample translation and rotation’. Int. Conf. of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 2015, pp. 1–8.
  207. 207)
       • 150. Rathgeb, C., Breitinger, F., Busch, C., et al: ‘On application of bloom filters to iris biometrics’, IET Biometrics, 2014, 3, (4), pp. 207–218.
  208. 208)
       • 32. Jain, A.K., Li, S.Z.: ‘Handbook of face recognition’ (Springer, UK, 2011).