Abstract
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.
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References
Ambeth Kumar VD, Ramakrishnan M (2012) Manifold feature extraction for foot print image. Indian J Bioinform Biotechnol 1(2):28–31
Barker SL, Scheuer JL (1998) Predictive value of human footprints in a forensic context. Med Sci Law 38(4):341–346
Biel L, Pettersson O, Philipson L, Wide P (2001) Ecg analysis: a new approach in human identification. IEEE Trans Instrum Meas 50(3):808–812
Boles WW, Boashash B (1998) A human identification technique using images of the iris and wavelet transform. IEEE Trans Signal Process 46(4):1185–1188
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):1160–1165
Choras M (2015) Ear biometrics. Encyclopedia of Biometrics, pp 363–368
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 1589–1592
Dietz HP (2004) Ultrasound imaging of the pelvic floor. part ii: three-dimensional or volume imaging. Ultrasound Obstet Gynecol 23(6):615–625
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 496–499
Han C-C, Cheng H-L, Lin C-L, Fan K-C (2003) Personal authentication using palm-print features. Pattern Recogn 36(2):371–381
Haralick RM, Shanmugam K, et al. (1973) Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC- 3(6):610–621
Hashem KM, Ghali F (2016) Human identification using foot features. Int J Eng Manuf 6(4):22–31
https://archive.mid-day.com/news/2012/mar/020312-theft-at-temple.htm (2012)
https://www.bbc.com/news/world-asia-india-42743982 (2018). [Online; accessed 02-October-2018]
ITU Telecom (1996) Standardization sector of itu,. Video coding for low bitrate communication, Draft ITU-T Recommendation H, pp 263
Jain A, Zongker D (1997) Feature selection: Evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158
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 1–8
Joachims T (1998) Text categorization with support vector machines: Learning with many relevant features. In: European conference on machine learning. Springer, pp 137–142
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 706–714
Kennedy RB (1996) Uniqueness of bare feet and its use as a possible means of identification. Forens Sci Int 82(1):81–87
Keogh E, Ratanamahatana CA (2005) Exact indexing of dynamic time warping. Knowl Inf Syst 7(3):358–386
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 16–26
Khokher R, Singh RC (2016) Footprint-based personal recognition using scanning technique. Indian Journal of Science and Technology, 9(44)
Ko K (2007) User’s guide to nist biometric image software (nbis). Technical report
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 45–55
Kumar A, Zhou Y (2012) Human identification using finger images. IEEE Trans Image Process 21(4):2228–2244
Kumar VDA, Ramakrishnan M (2013) Employment of footprint recognition system. Indian Journal of Computer Science and Engineering (IJCSE), 3(6)
Kushwaha R, Nain N (2012) Facial expression recognition. Int J Curr Eng Technol 2(2):270–278
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 164–171
Kushwaha R, Nain N (2018) Person identification using footprint minutiae. In: 2018 3rd international conference on Computer vision and image processing (CVIP). Springer
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 196–201
Li W, Zhang D, Xu Z (2002) Palmprint identification by fourier transform. Int J Pattern Recognit Artif Intell 16(04):417–432
Liu S, Silverman M (2001) A practical guide to biometric security technology. IT Prof 3(1):27– 32
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):1–18
Müller M (2007) Dynamic time warping. Information retrieval for music and motion, pp 69–84
Nagwanshi KK, Dubey S (2012) Biometric authentication using human footprint. Int J Appl Inf Syst (IJAIS) 3(7):1–6
Nakajima K, Mizukami Y, Tanaka K, Tamura T (2000) Footprint-based personal recognition. IEEE Trans Biomed Eng 47(11):1534–1537
Pavešić N, Ribarić S, Ribarić D (2004) Personal authentication using hand-geometry and palmprint features–the state of the art. Hand 11:12
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 1–8
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 47–56
Scholkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press
Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222
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)
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300
Tuceryan M, Jain AK (1993) Texture analysis. In: Handbook of pattern recognition and computer vision. World Scientific, pp 235–276
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 586–591
Uhl A, Wild P (2008) Footprint-based biometric verification. J Electron Imaging 17(1):011016
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 1–29
Wang L (2005) Support vector machines: theory and applications. Springer Science & Business Media, vol 177
Weingaertner D, Bellon ORP, Silva L, Cat MNL (2008) Newborn’s biometric identification: Can it be done?. In: VISAPP (1), pp 200–205
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):442–450
Yan P, Bowyer KW (2007) Biometric recognition using 3d ear shape. IEEE Trans Pattern Anal Mach Intell 29(8):1297–1308
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):1474
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Kushwaha, R., Nain, N. PUG-FB : Person-verification using geometric and Haralick features of footprint biometric. Multimed Tools Appl 79, 2671–2701 (2020). https://doi.org/10.1007/s11042-019-08149-0
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DOI: https://doi.org/10.1007/s11042-019-08149-0