An Overview of Biometrics Methods

  • Muhammad Sharif
  • Mudassar Raza
  • Jamal Hussain Shah
  • Mussarat Yasmin
  • Steven Lawrence Fernandes


Biometrics is becoming an important technology in automated person recognition. With the help of biometrics, the individuals are recognized through their unique characteristics and behaviors of various body parts. Some most famous biometrics techniques include the recognition of face, finger prints, iris, gate and signature. This chapter encompasses various biometrics methods used by researchers till date. The chapter depicts the biometrics under various categories such as biological and behavioral biometrics. This will help the readers to consider various biometrics while designing human recognition systems. Apart from the benefits, biometrics is also susceptible to hacking. The authors’ findings with benefits and drawbacks of biometrics are also discussed in this chapter.


Biometrics Overview Categories Recognition Uniqueness 


  1. 1.
    R. Alexander, Using the Analytical Hierarchy Process Model in the Prioritization of Information Assurance Defense In-Depth Measures?—A Quantitative Study, Journal of Information Security, 8 (2017) 166.CrossRefGoogle Scholar
  2. 2.
    D.W. Sanders, S.K. Kaufman, B.B. Holmes, M.I. Diamond, Prions and protein assemblies that convey biological information in health and disease, Neuron, 89 (2016) 433-448.CrossRefGoogle Scholar
  3. 3.
    M.W. Khan, M. Sharif, M. Yasmin, S.L. Fernandes, A new approach of cup to disk ratio based glaucoma detection using fundus images, Journal of Integrated Design and Process Science, 20 (2016) 77-94.CrossRefGoogle Scholar
  4. 4.
    R.P. Wildes, Iris recognition: an emerging biometric technology, Proceedings of the IEEE, 85 (1997) 1348-1363.CrossRefGoogle Scholar
  5. 5.
    J.M. Colores-Vargas, M. García-Vázquez, A. Ramírez-Acosta, H. Pérez-Meana, M. Nakano-Miyatake, Video images fusion to improve iris recognition accuracy in unconstrained environments, Mexican Conference on Pattern Recognition, (Springer2013), pp. 114-125.Google Scholar
  6. 6.
    M. Sharif, M.A. Ali, M. Raza, S. Mohsin, Face recognition using edge information and DCT, Sindh University Research Journal-SURJ (Science Series), 43 (2015).Google Scholar
  7. 7.
    J.H. Shah, M. Sharif, M. Raza, A. Azeem, A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques, Int. Arab J. Inf. Technol., 10 (2013) 536-545.Google Scholar
  8. 8.
    L. Ma, T. Tan, Y. Wang, D. Zhang, Efficient iris recognition by characterizing key local variations, IEEE Transactions on Image processing, 13 (2004) 739-750.CrossRefGoogle Scholar
  9. 9.
    F. Bokhari, T. Syedia, M. Sharif, M. Yasmin, S.L. Fernandes, Fundus image segmentation and feature extraction for the detection of glaucoma: a new approach, Current Medical Imaging Reviews, 14 (2018) 77-87.Google Scholar
  10. 10.
    P. Cofta, H. Lacohée, Understanding public perceptions: trust and engagement in ICT-mediated services (Intl. Engineering Consortiu, 2008).Google Scholar
  11. 11.
    S. Akbar, M.U. Akram, M. Sharif, A. Tariq, U. ullah Yasin, Decision support system for detection of papilledema through fundus retinal images, Journal of medical systems, 41 (2017) 66.Google Scholar
  12. 12.
    A. Panwar, P. Singla, M. Kaur, Techniques for Enhancing the Security of Fuzzy Vault: A Review, Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, (Springer, 2018), pp. 205-213.Google Scholar
  13. 13.
    A. Manickam, E. Devarasan, G. Manogaran, M.K. Priyan, R. Varatharajan, C.-H. Hsu, R. Krishnamoorthi, Score level based latent fingerprint enhancement and matching using SIFT feature, Multimedia Tools and Applications, (2018) 1-21.Google Scholar
  14. 14.
    M. Sharif, F. Naz, M. Yasmin, M.A. Shahid, A. Rehman, Face Recognition: A Survey, Journal of Engineering Science & Technology Review, 10 (2017).Google Scholar
  15. 15.
    J. Hussain Shah, M. Sharif, M. Raza, M. Murtaza, S. Ur-Rehman, Robust Face Recognition Technique under Varying Illumination, Journal of applied research and technology, 13 (2015) 97-105.CrossRefGoogle Scholar
  16. 16.
    J.H. Shah, M. Sharif, M. Raza, A. Azeem, Face recognition across pose variation and the 3S problem, turkish journal of electrical engineering & computer sciences, 22 (2014) 1423-1436.Google Scholar
  17. 17.
    A. Aisha, S. Muhammad, S.J. Hussain, R. Mudassar, Face recognition invariant to partial occlusions, KSII Trans. Internet Inf. Syst.((TIIS)), 8 (2014) 2496-2511.Google Scholar
  18. 18.
    M. Murtaza, M. Sharif, M. Raza, J. Shah, Face recognition using adaptive margin fisher’s criterion and linear discriminant analysis, International Arab Journal of Information Technology, 11 (2014) 1-11.Google Scholar
  19. 19.
    M. Sharif, J.H. Shah, S. Mohsin, M. Raza, Facial Feature Detection and Recognition for Varying Poses, World Congress on Engineering and Computer Science2014), pp. 22-24.Google Scholar
  20. 20.
    A. Azeem, M. Sharif, M. Raza, M. Murtaza, A survey: Face recognition techniques under partial occlusion, Int. Arab J. Inf. Technol., 11 (2014) 1-10.Google Scholar
  21. 21.
    Large-scale dynamic face recognition system, 2014).Google Scholar
  22. 22.
    M. Sharif, A. Khalid, M. Raza, S. Mohsin, Face detection and recognition through hexagonal image processing, Sindh University Research Journal-SURJ (Science Series), 44 (2012).Google Scholar
  23. 23.
    M. Sharif, S. Mohsin, M.Y. Javed, A survey: face recognition techniques, Research Journal of Applied Sciences, Engineering and Technology, 4 (2012) 4979-4990.Google Scholar
  24. 24.
    M. Sharif, S. Mohsin, M.Y. Javed, M.A. Ali, Single Image Face Recognition Using Laplacian of Gaussian and Discrete Cosine Transforms, Int. Arab J. Inf. Technol., 9 (2012) 562-570.Google Scholar
  25. 25.
    M. Sharif, M.Y. Javed, S. Mohsin, Face recognition based on facial features, Research Journal of Applied Sciences, Engineering and Technology, 4 (2012) 2879-2886.Google Scholar
  26. 26.
    M. Sharif, K. Ayub, D. Sattar, M. Raza, S. Mohsin, Enhanced and fast face recognition by hashing algorithm, Journal of applied research and technology, 10 (2012) 607-617.Google Scholar
  27. 27.
    M. Sharif, S. Anis, M. Raza, S. Mohsin, Enhanced SVD Based Face Recognition, Journal of Applied Computer Science & Mathematics, (2012).Google Scholar
  28. 28.
    M. Sharif, A. Khalid, M. Raza, S. Mohsin, Face Recognition using Gabor Filters, Journal of Applied Computer Science & Mathematics, (2011).Google Scholar
  29. 29.
    M. Sharif, S. Mohsin, R.A. Hanan, M.Y. Javed, M. Raza, Using nose heuristics for efficient face recognition, Sindh University Research Journal-SURJ (Science Series), 43 (2011).Google Scholar
  30. 30.
    M. Sharif, S. Mohsin, M.J. Jamal, M.Y. Javed, M. Raza, Face recognition for disguised variations using gabor feature extraction, Australian Journal of Basic and Applied Sciences, 5 (2011) 1648-1656.Google Scholar
  31. 31.
    M. Sharif, S. Mohsin, M.J. Jamal, M. Raza, Illumination Normalization Preprocessing for face recognition, Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on, (IEEE2010), pp. 44-47.Google Scholar
  32. 32.
    D.P. Chowdhury, S. Bakshi, G. Guo, P.K. Sa, On applicability of tunable filter bank based feature for ear biometrics: a study from constrained to unconstrained, Journal of medical systems, 42 (2018) 11.Google Scholar
  33. 33.
    D. Zhang, G. Lu, L. Zhang, Online 3D Ear Recognition, Advanced Biometrics, (Springer, 2018), pp. 309-328.Google Scholar
  34. 34.
    M. Boczek, Ear biometric capture, authentication, and identification method and system, (Google Patents2017).Google Scholar
  35. 35.
    I.B. Barbosa, T. Theoharis, A.E. Abdallah, On the use of fingernail images as transient biometric identifiers, Machine Vision and Applications, 27 (2016) 65-76.Google Scholar
  36. 36.
    A. Uhl, P. Wild, Footprint-based biometric verification, Journal of Electronic Imaging, 17 (2008) 011016.CrossRefGoogle Scholar
  37. 37.
    X. Wang, H. Wang, Q. Cheng, N.L. Nankabirwa, T. Zhang, Single 2D pressure footprint based person identification, Biometrics (IJCB), 2017 IEEE International Joint Conference on, (IEEE2017), pp. 413-419.Google Scholar
  38. 38.
    A. Brown, DNA as an investigative technique, Science and Justice, 38 (1998) 263-265.CrossRefGoogle Scholar
  39. 39.
    C. Forr, B. Schei, L.E. Stene, K. Ormstad, C.T. Hagemann, Factors associated with trace evidence analyses and DNA findings among police reported cases of rape, Forensic science international, 283 (2018) 136-143.CrossRefGoogle Scholar
  40. 40.
    M. Shirasu, K. Touhara, The scent of disease: volatile organic compounds of the human body related to disease and disorder, The Journal of Biochemistry, 150 (2011) 257-266.CrossRefGoogle Scholar
  41. 41.
    S. Haze, Y. Gozu, S. Nakamura, Y. Kohno, K. Sawano, H. Ohta, K. Yamazaki, 2-Nonenal newly found in human body odor tends to increase with aging, Journal of investigative dermatology, 116 (2001) 520-524.CrossRefGoogle Scholar
  42. 42.
    I. Rodriguez-Lujan, G. Bailador, C. Sanchez-Avila, A. Herrero, G. Vidal-De-Miguel, Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics, Knowledge-Based Systems, 52 (2013) 279-289.CrossRefGoogle Scholar
  43. 43.
    Hand, finger geometry, 2018).Google Scholar
  44. 44.
    C.M. Travieso, J. Zhang, P. Miller, J.B. Alonso, M.A. Ferrer, Bimodal biometric verification based on face and lips, Neurocomputing, 74 (2011) 2407-2410.CrossRefGoogle Scholar
  45. 45.
    Y. Vasquez, C. Beltrán, M. Gómez, M. Flórez, J.L. Vázquez-González, Features extraction in images on finger veins with hybrid curves, Humanitarian Technology Conference (MHTC), IEEE Mexican, (IEEE2017), pp. 34-38.Google Scholar
  46. 46.
    W. Yang, S. Wang, J. Hu, G. Zheng, C. Valli, A fingerprint and finger-vein based cancelable multi-biometric system, Pattern Recognition, 78 (2018) 242-251.CrossRefGoogle Scholar
  47. 47.
    P. Gupta, S. Srivastava, P. Gupta, An accurate infrared hand geometry and vein pattern based authentication system, Knowledge-Based Systems, 103 (2016) 143-155.CrossRefGoogle Scholar
  48. 48.
    E.J. Esekhaigbe, Contributions to Biometric Recognition: Fingerprint For Identity Verification, Cardiff Metropolitan University, 2016.Google Scholar
  49. 49.
    D.-H. Park, B.J. Park, J.-M. Kim, Hydrochromic approaches to mapping human sweat pores, Accounts of chemical research, 49 (2016) 1211-1222.CrossRefGoogle Scholar
  50. 50.
    A. Genovese, E. Munoz, V. Piuri, F. Scotti, G. Sforza, Towards touchless pore fingerprint biometrics: a neural approach, Evolutionary Computation (CEC), 2016 IEEE Congress on, (IEEE2016), pp. 4265-4272.Google Scholar
  51. 51.
    M.-j. Kim, W.-Y. Kim, J. Paik, Optimum Geometric Transformation and Bipartite Graph-Based Approach to Sweat Pore Matching for Biometric Identification, Symmetry, 10 (2018) 175.Google Scholar
  52. 52.
    P. Campisi, D. La Rocca, Brain waves for automatic biometric-based user recognition, IEEE transactions on information forensics and security, 9 (2014) 782-800.CrossRefGoogle Scholar
  53. 53.
    P.J. García-Laencina, G. Rodríguez-Bermudez, J. Roca-Dorda, Exploring dimensionality reduction of EEG features in motor imagery task classification, Expert Systems with Applications, 41 (2014) 5285-5295.CrossRefGoogle Scholar
  54. 54.
    S. Romero, M. Mañanas, M. Barbanoj, Ocular reduction in EEG signals based on adaptive filtering, regression and blind source separation, Annals of biomedical engineering, 37 (2009) 176-191.CrossRefGoogle Scholar
  55. 55.
    K. Phua, J. Chen, T.H. Dat, L. Shue, Heart sound as a biometric, Pattern Recognition, 41 (2008) 906-919.CrossRefGoogle Scholar
  56. 56.
    M.S.N. Dere, A. Gurjar, Identification of Human using Palm-Vein Images: A new trend in biometrics, International Journal Of Engineering And Computer Science ISSN, 2319-7242.Google Scholar
  57. 57.
    V.S. Nalwa, Automatic on-line signature verification, Proceedings of the IEEE, 85 (1997) 215-239.CrossRefGoogle Scholar
  58. 58.
    D.B.S. Netto, M. Fornazin, M.A. Cavenaghi, R. Spolon, R.S. Lobato, A practical approach for biometric authentication based on smartcards, Information Systems and Technologies (CISTI), 2010 5th Iberian Conference on2010), pp. 1-5.Google Scholar
  59. 59.
    M. Sharif, M.A. Khan, M. Faisal, M. Yasmin, S.L. Fernandes, A framework for offline signature verification system: Best features selection approach, Pattern Recognition Letters, (2018).Google Scholar
  60. 60.
    M. Khitrov, Talking passwords: voice biometrics for data access and security, Biometric Technology Today, 2013 (2013) 9-11.CrossRefGoogle Scholar
  61. 61.
    K.S.R. Murty, B. Yegnanarayana, Combining evidence from residual phase and MFCC features for speaker recognition, IEEE signal processing letters, 13 (2006) 52-55.CrossRefGoogle Scholar
  62. 62.
    M. Bezoui, A. Elmoutaouakkil, A. Beni-hssane, Feature extraction of some Quranic recitation using mel-frequency cepstral coeficients (MFCC), Multimedia Computing and Systems (ICMCS), 2016 5th International Conference on, (IEEE2016), pp. 127-131.Google Scholar
  63. 63.
    N. Almaadeed, A. Aggoun, A. Amira, Speaker identification using multimodal neural networks and wavelet analysis, IET Biometrics, 4 (2015) 18-28.CrossRefGoogle Scholar
  64. 64.
    L. Lu, L. Liu, M.J. Hussain, Y. Liu, I sense you by Breath: Speaker Recognition via Breath Biometrics, IEEE Transactions on Dependable and Secure Computing, (2017) 1-1.Google Scholar
  65. 65.
    J. Chauhan, Y. Hu, S. Seneviratne, A. Misra, A. Seneviratne, Y. Lee, BreathPrint: Breathing acoustics-based user authentication, Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, (ACM2017), pp. 278-291.Google Scholar
  66. 66.
    D. Stewart, A. Pass, J. Zhang, Gender classification via lips: static and dynamic features, IET biometrics, 2 (2013) 28-34.CrossRefGoogle Scholar
  67. 67.
    S.-L. Wang, A.W.-C. Liew, Physiological and behavioral lip biometrics: A comprehensive study of their discriminative power, Pattern Recognition, 45 (2012) 3328-3335.CrossRefGoogle Scholar
  68. 68.
    M. Raza, M. Iqbal, M. Sharif, W. Haider, A survey of password attacks and comparative analysis on methods for secure authentication, World Applied Sciences Journal, 19 (2012) 439-444.Google Scholar
  69. 69.
    M. Sharif, T. Faiz, M. Raza, Time signatures-an implementation of keystroke and click patterns for practical and secure authentication, Digital Information Management, 2008. ICDIM 2008. Third International Conference on, (IEEE2008), pp. 559-562.Google Scholar
  70. 70.
    S.P. Banerjee, D.L. Woodard, Biometric authentication and identification using keystroke dynamics: A survey, Journal of Pattern Recognition Research, 7 (2012) 116-139.CrossRefGoogle Scholar
  71. 71.
    M.H. Khan, F. Li, M.S. Farid, M. Grzegorzek, Gait recognition using motion trajectory analysis, International Conference on Computer Recognition Systems, (Springer2017), pp. 73-82.Google Scholar
  72. 72.
    S. Yu, H. Chen, Q. Wang, L. Shen, Y. Huang, Invariant feature extraction for gait recognition using only one uniform model, Neurocomputing, 239 (2017) 81-93.CrossRefGoogle Scholar
  73. 73.
    S.D. Choudhury, T. Tjahjadi, Clothing and carrying condition invariant gait recognition based on rotation forest, Pattern Recognition Letters, 80 (2016) 1-7.CrossRefGoogle Scholar
  74. 74.
    R. Amin, T. Gaber, G. ElTaweel, A.E. Hassanien, Biometric and traditional mobile authentication techniques: Overviews and open issues, Bio-inspiring cyber security and cloud services: trends and innovations, (Springer, 2014), pp. 423-446.Google Scholar
  75. 75.
    J.N. Mogan, C.P. Lee, A.W. Tan, Gait recognition using temporal gradient patterns, Information and Communication Technology (ICoIC7), 2017 5th International Conference on, (IEEE2017), pp. 1-4.Google Scholar
  76. 76.
    C.-C. Huang, C.-C. Hsu, H.-Y. Liao, S.-H. Yang, L.-L. Wang, S.-Y. Chen, Frontal gait recognition based on spatio-temporal interest points, Journal of the Chinese Institute of Engineers, 39 (2016) 997-1002.CrossRefGoogle Scholar
  77. 77.
    S. Tao, X. Zhang, H. Cai, Z. Lv, C. Hu, H. Xie, Gait based biometric personal authentication by using MEMS inertial sensors, Journal of Ambient Intelligence and Humanized Computing, (2018) 1-8.Google Scholar
  78. 78.
    A. Byrnes, A. Mudge, A. Young, M. Banks, J. Bauer, Use of hand grip strength in nutrition risk screening of older patients admitted to general surgical wards, Nutrition & Dietetics, (2018).Google Scholar
  79. 79.
    A. Wichelhaus, C. Harms, J. Neumann, S. Ziegler, G. Kundt, K.J. Prommersberger, T. Mittlmeier, M. Mühldorfer-Fodor, Parameters influencing hand grip strength measured with the manugraphy system, BMC musculoskeletal disorders, 19 (2018) 54.Google Scholar
  80. 80.
    M.S. Islam, M. Ali, K.H. Zubaer, S. Sarmin, M.T. Islam, B. Islam, A.A. Al Islam, A.M. Sadri, Trusted Worrier: A low-cost and high-accuracy user authentication system for firearm exploiting dynamic hand pressure biometrics, Networking, Systems and Security (NSysS), 2017 International Conference on, (IEEE2017), pp. 87-95.Google Scholar
  81. 81.
    B. Çakmak, E. Ergül, Interactions of personal and occupational risk factors on hand grip strength of winter pruners, International Journal of Industrial Ergonomics, 67 (2018) 192-200.CrossRefGoogle Scholar
  82. 82.
    K. Howell, 3 Reasons Biometrics Are Not Secure, (ipswitch2017).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Muhammad Sharif
    • 1
  • Mudassar Raza
    • 1
  • Jamal Hussain Shah
    • 1
  • Mussarat Yasmin
    • 1
  • Steven Lawrence Fernandes
    • 2
  1. 1.COMSATS University IslamabadWah CampusPakistan
  2. 2.Department of Electronics and Communication EngineeringSahyadri College of Engineering and ManagementMangaluruIndia

Personalised recommendations