Foot biometry for personal recognition has not gained much fame compared to other biometric modalities like face, palmprint, fingerprint, gait and iris etc. Extraction of texture features of human footprints is the primary focus of this investigation. This paper puts forward an innovative approach for footprint biometrics using feature extraction methods. For the purpose, it uses chronological implementation of LBP and SIFT, the potent texture based feature extraction methods for object recognition / detection. Features of footprints are digged out using these methods pursued by matching and recognition. Experimental results demonstrate that features derived using this approach showed better recognition performance and achieved improved recognition accuracy.

biometrics, footprint recognition, feature extraction, LBP and SIFT.

Received: May 17, 2021; Accepted: June 24, 2021; Published: August 5, 2021

How to cite this article: Anshu Gupta and Deepa Raj, Footprint Recognition Using Invariant Feature Extraction Techniques, Far East Journal of Electronics and Communications 24(2) (2021), 81-108. DOI: 10.17654/EC024020081

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

References:

[1] A. K. Jain, A. Ross and S. Pankanti, Biometrics: a tool for information security, IEEE Transactions on Information Forensics and Security 1(2) (2006), 125-143.
[2] R. B. Kennedy, Uniqueness of bare feet and its use as a possible means of identification, Forensic Science International 82(1) (1996), 81-87.
DOI: 10.1016/0379-0738(96)01969-x.
[3] K. Nakajima, Y. Mizukami, K. Tanaka and T. Tamura, Footprint-based personal recognition, IEEE Transactions on Biomedical Engineering 47(11) (2000), 1534-1537. doi: 10.1109/10.880106.
[4] A. Uhl and P. Wild, Footprint-based biometric verification, Journal of Electronic Imaging 17(1) (2008), 011-016.
[5] S. Zheng, K. Huang and T. Tan, Evaluation framework on translation-invariant representation for cumulative foot pressure image, 18th IEEE International Conference on Image Processing (2011), 201-204.
doi: 10.1109/ICIP.2011.6115874.
[6] W. Jia, H. Y. Cai, J. Gui, R. X. Hu, Y. K. Lei and X. F. Wang, Newborn footprint recognition using orientation feature, Neural Computing and Applications 21(8) (2012), 1855-1863. doi:10.1007/s00521-011-0530-9.
[7] V. D. A. Kumar and M. Ramakrishnan, Legacy of Footprints Recognition - A Review, International Journal of Computer Applications 35 (2011), 9-16.
[8] K. K. Nagwanshi and S. Dubey, Biometric authentication using human footprint, International Journal of Applied Information Systems (IJAIS) 3(7) (2012), 1-6.
[9] S. Zheng, K. Huang, T. Tan and D. Tao, A cascade fusion scheme for gait and cumulative foot pressure image recognition, Pattern Recognition 45(10) (2012), 3603-3610.
[10] V. A. Kumar, M. Ramakrishnan and R. J. Kannan, Temple and maternity ward security using FPRS, Journal of Electrical Engineering & Technology 8(3) (2013), 633-7.
[11] S. R. Zhou, J. P. Yin and J. M. Zhang, Local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation, Neurocomputing 116 (2013), 260-264.
[12] R. Vera-Rodriguez, J. S. Mason, J. Fierrez and J. Ortega-Garcia, Comparative analysis and fusion of spatiotemporal information for footstep recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 35(4) (2012), 823-834.
[13] R. Vera-Rodriguez, J. Fierrez, J. S. Mason and J. Orteua-Garcia, A novel approach of gait recognition through fusion with footstep information, 2013 International Conference on Biometrics (ICB) IEEE (2013), 1-6.
[14] R. R. King and W. Xiaopeng, Study of biometric identification method based on naked footprint, International Journal of Science and Engineering 5(2) (2013), 29-35.
[15] J. Kotzerke, S. Davis, K. Horadam and J. McVernon, Newborn and infant footprint crease pattern extraction, 2013 IEEE International Conference on Image Processing (2013), 4181-4185.
[16] T. N. Moorthy, A. M. B. Mostapa, R. Boominathan and N. Raman, Stature estimation from footprint measurements in Indian Tamils by regression analysis, Egyptian Journal of Forensic Sciences 4(1) (2014), 7-16.
[17] T. N. Moorthy and S. F. B. Sulaiman, Individualizing characteristics of footprints in Malaysian Malays for person identification from a forensic perspective, Egyptian Journal of Forensic Sciences 5(1) (2015), 13-22.
[18] M. Edwards and X. Xie, Footstep pressure signal analysis for human identification, 7th International Conference on Biomedical Engineering and Informatics (2014), 307-312.
[19] A. K. V. Devadoss, R. Malaishamy, M. Subramaniam and A. K. V. Devadoss, Performance improvement using an automation system for recognition of multiple parametric features based on human footprint, Kuwait Journal of Science 42(1) (2015).
[20] V. A. Kumar, S. Malathi, V. A. Kumar and P. Kannan, Performance improvement using an automation system for segmentation of multiple parametric features based on human footprint, Journal of Electrical Engineering & Technology 10(4) (2015), 1815-1821.
[21] R. Khokher, R. C. Singh and R. Kumar, Footprint recognition with principal component analysis and independent component analysis, Macromolecular Symposia 347(1) (2015), 16-26.
[22] M. Costea, B. Sarghie, A. Mihai and E. Rezus, Classification of the elderly foot types based on plantar footprints, Procedia Engineering 181 (2017), 36-43.
[23] S. B. Verma and S. Chandran, Analysis of SIFT and SURF feature extraction in palmprint verification system, IEEE International Conference on Computing, Communication and Control Technology (IC4T) (2016), 27-30.
[24] O. Costilla-Reyes, R. Vera-Rodriguez, P. Scully and K. B. Ozanyan, Spatial footstep recognition by convolutional neural networks for biometric applications, 2016 IEEE SENSORS (2016), 1-3.
[25] R. Khokher and R. C. Singh, Footprint-based personal recognition using Dactyloscopy technique, Industrial Mathematics and Complex Systems (2017), 207-219.
[26] K. K. Nagwanshi and S. Dubey, Statistical feature analysis of human footprint for personal identification using bigML and IBM Watson analytics, Arabian Journal for Science and Engineering 43(6) (2018), 2703-2712.
[27] S. B. Verma and S. Chandran, Touchless region based palmprint verification system, International Journal of Computer Science and Information Security (IJCSIS) 15(4) (2017).
[28] E. Liu, Infant footprint recognition, Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 1653-1660.
[29] K. K. Nagwanshi, Cyber forensic review of human footprint and gait-based system for personal identification in crime scene investigation, Preprint 2018. doi:10.20944/preprints201804.0072.v1.
[30] R. Kushwaha, N. Nain and G. Singal, Mira: Moment invariability analysis of footprint features, 2018 IEEE 8th International Advance Computing Conference (IACC) (2018), 196-201.
[31] O. Costilla-Reyes, R. Vera-Rodriguez, P. Scully and K. B. Ozanyan, Analysis of spatio-temporal representations for robust footstep recognition with deep residual neural networks, IEEE Transactions on Pattern Analysis and Machine Intelligence 41(2) (2018), 285-296.
[32] M. M. Abuqadumah, M. A. Ali, A. Abd Almisreb and B. Durakovic, Deep transfer learning for human identification based on footprint: a comparative study, Periodicals of Engineering and Natural Sciences 7(3) (2019), 1300-1307.
[33] Y. I. brahim and I. M. Alhamdani, A hyprid technique for human footprint recognition, International Journal of Electrical & Computer Engineering 9 (2019), 2088-8708.
[34] R. Kushwaha, G. Singal and N. Nain, A texture feature based approach for person verification using footprint bio-metric, Artificial Intelligence Review (2020), 1-31.
[35] T. Ojala, M. Pietikäinen and T. Mäenpää, Gray scale and rotation invariant texture classification with local binary patterns, European Conference on Computer Vision, Springer, Berlin, Heidelberg, 2000, pp. 404-420.
[36] T. Ojala, M. Pietikainen and T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7) (2002), 971-987.
[37] D. G. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60(2) (2004), 91-110.
[38] J. Malik, D. Girdhar, R. Dahiya and G. Sainarayanan, Reference threshold calculation for biometric authentication, International Journal of Image, Graphics and Signal Processing 6(2) (2014), 46.