PUG-FB : Person-verification using geometric and Haralick features of footprint biometric

  • Riti KushwahaEmail author
  • Neeta Nain


This article demonstrates a study of biometric identification and verification system using foot geometry features. A footprint has three types of features which are sufficient to recognize a person uniquely. These features are categorized into geometric, texture, and minutiae. We have computed most widely used geometry features of the foot using length, width, area, major axis, and minor axis, to identify a person uniquely. Different variations of these features are computed by assigning weights to each feature emphasizing its importance. We have extracted the best variations among foot descriptors, and conclude that the province is the most contributing factor to identify a person foot uniquely. Foot contour features are further combined with foot descriptors to increase the accuracy. For texture, Gray level co-occurrence matrix based on Haralick features is computed with Support Vector Machine as the classifier. Foot biometrics can be used as an additional covert authentication measure where people remove shoes, such as holy places, airport security, swimming pools, wellness centers etc. It can also be used for newborn authentication and identification in hospitals. The method achieves GenuineAcceptRate(GAR) of 82% with the FalseAcceptRate(FAR) of 2.0%, and GAR of 85% with the FAR of 4.0% in case of combination sum rule. GenuineAcceptRate(GAR) has increased to 87.5% at FalseAcceptRate(FAR) of 2.0% including texture features as Gray level co-occurrence matrix.


Footprint Biometrics Geometry feature Dynamic time warp Haralick features 



  1. 1.
    Ambeth Kumar VD, Ramakrishnan M (2012) Manifold feature extraction for foot print image. Indian J Bioinform Biotechnol 1(2):28–31Google Scholar
  2. 2.
    Barker SL, Scheuer JL (1998) Predictive value of human footprints in a forensic context. Med Sci Law 38(4):341–346CrossRefGoogle Scholar
  3. 3.
    Biel L, Pettersson O, Philipson L, Wide P (2001) Ecg analysis: a new approach in human identification. IEEE Trans Instrum Meas 50(3):808–812CrossRefGoogle Scholar
  4. 4.
    Boles WW, Boashash B (1998) A human identification technique using images of the iris and wavelet transform. IEEE Trans Signal Process 46(4):1185–1188CrossRefGoogle Scholar
  5. 5.
    Chang K, Bowyer KW, Sarkar S, Victor B (2003) Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans Pattern Anal Mach Intell 25(9):1160–1165CrossRefGoogle Scholar
  6. 6.
    Choras M (2015) Ear biometrics. Encyclopedia of Biometrics, pp 363–368CrossRefGoogle Scholar
  7. 7.
    Cutler R, Davis L (2000) Look who’s talking: Speaker detection using video and audio correlation. In: 2000. ICME 2000. 2000 IEEE international conference on Multimedia and expo. IEEE, vol 3, pp 1589–1592Google Scholar
  8. 8.
    Dietz HP (2004) Ultrasound imaging of the pelvic floor. part ii: three-dimensional or volume imaging. Ultrasound Obstet Gynecol 23(6):615–625CrossRefGoogle Scholar
  9. 9.
    Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. In: 2008 5Th IEEE international symposium on biomedical imaging: From nano to macro. IEEE, pp 496–499Google Scholar
  10. 10.
    Han C-C, Cheng H-L, Lin C-L, Fan K-C (2003) Personal authentication using palm-print features. Pattern Recogn 36(2):371–381CrossRefGoogle Scholar
  11. 11.
    Haralick RM, Shanmugam K, et al. (1973) Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC- 3(6):610–621CrossRefGoogle Scholar
  12. 12.
    Hashem KM, Ghali F (2016) Human identification using foot features. Int J Eng Manuf 6(4):22–31Google Scholar
  13. 13.
  14. 14. (2018). [Online; accessed 02-October-2018]
  15. 15.
    ITU Telecom (1996) Standardization sector of itu,. Video coding for low bitrate communication, Draft ITU-T Recommendation H, pp 263Google Scholar
  16. 16.
    Jain A, Zongker D (1997) Feature selection: Evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158CrossRefGoogle Scholar
  17. 17.
    Jain AK, Cao K, Arora SS (2014) Recognizing infants and toddlers using fingerprints: Increasing the vaccination coverage. In: 2014 IEEE international joint conference on Biometrics (IJCB). IEEE, pp 1–8Google Scholar
  18. 18.
    Joachims T (1998) Text categorization with support vector machines: Learning with many relevant features. In: European conference on machine learning. Springer, pp 137–142Google Scholar
  19. 19.
    Kale A, Cuntoor N, Yegnanarayana B, Rajagopalan A N, Chellappa R (2003) Gait analysis for human identification. In: International conference on audio-and video-based biometric person authentication. Springer, pp 706–714Google Scholar
  20. 20.
    Kennedy RB (1996) Uniqueness of bare feet and its use as a possible means of identification. Forens Sci Int 82(1):81–87CrossRefGoogle Scholar
  21. 21.
    Keogh E, Ratanamahatana CA (2005) Exact indexing of dynamic time warping. Knowl Inf Syst 7(3):358–386CrossRefGoogle Scholar
  22. 22.
    Khokher R, Singh RC, Kumar R (2015) Footprint recognition with principal component analysis and independent component analysis. In: Macromolecular symposia. Wiley Online Library, vol 347, pp 16–26Google Scholar
  23. 23.
    Khokher R, Singh RC (2016) Footprint-based personal recognition using scanning technique. Indian Journal of Science and Technology, 9(44)Google Scholar
  24. 24.
    Ko K (2007) User’s guide to nist biometric image software (nbis). Technical reportGoogle Scholar
  25. 25.
    Kubanek M (2006) Method of speech recognition and speaker identification using audio-visual of polish speech and hidden markov models. In: Biometrics, computer security systems and artificial intelligence applications. Springer, pp 45–55Google Scholar
  26. 26.
    Kumar A, Zhou Y (2012) Human identification using finger images. IEEE Trans Image Process 21(4):2228–2244MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Kumar VDA, Ramakrishnan M (2013) Employment of footprint recognition system. Indian Journal of Computer Science and Engineering (IJCSE), 3(6)Google Scholar
  28. 28.
    Kushwaha R, Nain N (2012) Facial expression recognition. Int J Curr Eng Technol 2(2):270–278Google Scholar
  29. 29.
    Kushwaha R, Nain N, Gupta SK (2016) Person identification on the basis of footprint geometry. In: 2016 12th international conference on Signal-image technology & internet-based systems (SITIS). IEEE, pp 164–171Google Scholar
  30. 30.
    Kushwaha R, Nain N (2018) Person identification using footprint minutiae. In: 2018 3rd international conference on Computer vision and image processing (CVIP). SpringerGoogle Scholar
  31. 31.
    Kushwaha R, Nain N, Singal G (2018) Mira: Moment invariability analysis of footprint features. In: 2018 IEEE 8Th international advance computing conference (IACC). IEEE, pp 196–201Google Scholar
  32. 32.
    Li W, Zhang D, Xu Z (2002) Palmprint identification by fourier transform. Int J Pattern Recognit Artif Intell 16(04):417–432CrossRefGoogle Scholar
  33. 33.
    Liu S, Silverman M (2001) A practical guide to biometric security technology. IT Prof 3(1):27– 32CrossRefGoogle Scholar
  34. 34.
    Moos S, Marcolin F, Tornincasa S, Vezzetti E, Violante MG, Fracastoro G, Speranza D, Padula F (2017) Cleft lip pathology diagnosis and foetal landmark extraction via 3d geometrical analysis. Int J Interact Des Manuf (IJIDeM) 11(1):1–18CrossRefGoogle Scholar
  35. 35.
    Müller M (2007) Dynamic time warping. Information retrieval for music and motion, pp 69–84Google Scholar
  36. 36.
    Nagwanshi KK, Dubey S (2012) Biometric authentication using human footprint. Int J Appl Inf Syst (IJAIS) 3(7):1–6Google Scholar
  37. 37.
    Nakajima K, Mizukami Y, Tanaka K, Tamura T (2000) Footprint-based personal recognition. IEEE Trans Biomed Eng 47(11):1534–1537CrossRefGoogle Scholar
  38. 38.
    Pavešić N, Ribarić S, Ribarić D (2004) Personal authentication using hand-geometry and palmprint features–the state of the art. Hand 11:12Google Scholar
  39. 39.
    Porebski A, Vandenbroucke N, Macaire L (2008) Haralick feature extraction from lbp images for color texture classification. In: 2008 First workshops on image processing theory, tools and applications. IEEE, pp 1–8Google Scholar
  40. 40.
    Savran A, Alyüz N, Dibeklioğlu H, Çeliktutan O, Gökberk B, Sankur B, Akarun L (2008) Bosphorus database for 3d face analysis. In: European workshop on biometrics and identity management. Springer, pp 47–56Google Scholar
  41. 41.
    Scholkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT PressGoogle Scholar
  42. 42.
    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222MathSciNetCrossRefGoogle Scholar
  43. 43.
    Sudiro SA, Yuwono RT (2012) Adaptable fingerprint minutiae extraction algorithm based-on crossing number method for hardware implementation using fpga device. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2(3)CrossRefGoogle Scholar
  44. 44.
    Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRefGoogle Scholar
  45. 45.
    Tuceryan M, Jain AK (1993) Texture analysis. In: Handbook of pattern recognition and computer vision. World Scientific, pp 235–276Google Scholar
  46. 46.
    Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: 1991. Proceedings CVPR’91., IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 586–591Google Scholar
  47. 47.
    Uhl A, Wild P (2008) Footprint-based biometric verification. J Electron Imaging 17(1):011016CrossRefGoogle Scholar
  48. 48.
    Vezzetti E, Marcolin F, Tornincasa S, Ulrich L, Dagnes N (2017) 3d geometry-based automatic landmark localization in presence of facial occlusions. Multimedia Tools and Applications, pp 1–29Google Scholar
  49. 49.
    Wang L (2005) Support vector machines: theory and applications. Springer Science & Business Media, vol 177Google Scholar
  50. 50.
    Weingaertner D, Bellon ORP, Silva L, Cat MNL (2008) Newborn’s biometric identification: Can it be done?. In: VISAPP (1), pp 200–205Google Scholar
  51. 51.
    Wickenheiser RA (2002) Trace dna: a review, discussion of theory, and application of the transfer of trace quantities of dna through skin contact. J Forens Sci 47(3):442–450Google Scholar
  52. 52.
    Yan P, Bowyer KW (2007) Biometric recognition using 3d ear shape. IEEE Trans Pattern Anal Mach Intell 29(8):1297–1308CrossRefGoogle Scholar
  53. 53.
    Zhang X, Cui J, Wang W, Lin C (2017) A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors 17(7):1474CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Malaviya National Institute of TechnologyJaipurIndia

Personalised recommendations