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RETRACTED ARTICLE: Score level based latent fingerprint enhancement and matching using SIFT feature

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This article was retracted on 13 September 2022

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Abstract

Latent fingerprint identification is such a difficult task to law enforcement agencies and border security in identifying suspects. It is a too complicate due to poor quality images with non-linear distortion and complex background noise. Hence, the image quality is required for matching those latent fingerprints. The current researchers have been working based on minutiae points for fingerprint matching because of their accuracy are acceptable. In an effort to extend technology for fingerprint matching, our model is to propose the enhancementand matching for latent fingerprints using Scale Invariant Feature Transformation (SIFT). It has involved in two phases (i) Latent fingerprint contrast enhancement using intuitionistic type-2 fuzzy set (ii) Extract the SIFTfeature points from the latent fingerprints. Then thematching algorithm is performedwith n- number of images and scoresare calculated by Euclidean distance. We tested our algorithm for matching, usinga public domain fingerprint database such as FVC-2004 and IIIT-latent fingerprint. The experimental consequences indicatethe matching result is obtained satisfactory compare than minutiae points.

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References

  1. Arora S, Liu E, Cao K, Jain AK (2014) Latent fingerprint matching: performance gain via feedback from exemplar prints. IEEE Trans Pattern Anal Mach Intell 36(12):2452–2465

    Article  Google Scholar 

  2. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  Google Scholar 

  3. Babler WJ (1991) Embryologic development of epidermal ridges and their configurations. Birth Defects Orig Artic Ser 27:95–112

    Google Scholar 

  4. Bansal R, Arora P, Gaur M, Sehgal P, Bedi P (2009) Fingerprint image enhancement using type-2 fuzzy sets. Proceedings of the IEEE sixth international conference on fuzzy systems and knowledge discovery. 3:412–417. Tianjin

  5. Bustince H, Kacprzyk J, Mohedano V (2000) Intuitionistic fuzzy generators application to intuitionistic fuzzy complementation. Fuzzy Sets Syst 114(3):485–504

    Article  MathSciNet  Google Scholar 

  6. Cao K, Liu E, Jain AK (2014) Segmentation and enhancement of latent fingerprints: a coarse to fine ridge structure dictionary. IEEE Trans Pattern Anal Mach Intell 36(9):1847–1859

    Article  Google Scholar 

  7. Chaira T (2013) Contrast enhancement of medical images using type II fuzzy set. Proceedings of the IEEE national conference on communications. 1–5. India

  8. Greenberg S, Aladjem M, Kogan D, Dimitrov I (2000) Fingerprint image enhancement using filtering techniques. Proceedings of the 15th international conference on pattern recognition. 3, 322–325. Barcelona

  9. Jain AK, Feng J (2011) Latent fingerprint matching. IEEE Trans Pattern Anal Mach Intell 33(1):88–100

    Article  Google Scholar 

  10. Jain AK, Flynn P, Ross AA (2007) Handbook of biometrics. Springer Science and Business Media, New York

    Google Scholar 

  11. Jayaram B, Narayana K, Vetrivel V (2011) Fuzzy inference system based contrast enhancement. Proceedings of the international conference on EUSFLAT-LFA. 311–318. France

  12. Karimi AS, Kuo CC (2008) A robust technique for latent fingerprint image segmentation and enhancement. Proceeding of the IEEE international conference on image processing. 1492–1495. Canada

  13. Kumar, P. M., Gandhi, U., Varatharajan, R., Manogaran, G., Jidhesh, R., & Vadivel, T. (2017). Intelligent face recognition and navigation system using neural learning for smart security in internet of things. Clust Comput 1–12. doi:https://doi.org/10.1007/s10586-017-1323-4

  14. Lee KH (2006) First course on fuzzy theory and applications. Springer Science and Business Media, Germany

    Google Scholar 

  15. Liao X, Qin Z, Ding L (2017a) Dataembedding in digital images using critical functions. Signal Process Image Commun 58:146–156

    Article  Google Scholar 

  16. Liao X, Yin J, Guo S, Li X, Sangaiah AK (2017b) Medical JPEG image steganography based onpreserving inter-block dependencies. Comput Electr Eng. https://doi.org/10.1016/j.compel-eceng.2017.08.020

  17. Lopez, D. and Gunasekaran, M. (2015). Assessment of vaccination strategies using fuzzy multicriteriadecision making. In Proc. Proceedings of the Fifth International Conference on Fuzzy and NeuroComputing (FANCCO-2015). Springer International, Cham, pp. 195–208

  18. Lopez, D., & Manogaran, G. (2016). Big data architecture for climate change and disease dynamics. Geetam S. Tomar (eds.) et al. The human element of big data: issues, analytics, and performance. CRC Press, Florida

  19. Lopez, D., & Manogaran, G. (2017). Parametric model to predict H1N1 influenza in vellore district, Tamil Nadu, India. In handbook of statistics, vol. 37. Elsevier, Tamil Nadu, pp. 301–316

  20. Lopez D, Sekaran G (2016) Climate change and disease dynamics - a big data perspective. Int J Infect Dis 45:23–24

    Article  Google Scholar 

  21. Lopez, D., Gunasekaran, M., Murugan, B. S., Kaur, H., and Abbas, K. M. (2014, October). Spatial big data analytics of influenza epidemic in Vellore, India. In Proc. 2014 IEEE International Conference onBig Data. IEEE, pp. 19–24. doi: https://doi.org/10.1109/BigData.2014.7004422

  22. Lopez D, Manogaran G, Jagan J (2017) Modelling the H1N1 influenza using mathematical and neural network approaches. Biomed Res 28(8):1–5

    Google Scholar 

  23. Lowe DG (1999) Object recognition from local scale-invariant features. Proceedings of the seventh IEEE international conference on computer vision. 2:1150–1157. Kerkyra

  24. Malathi S, Meena C (2011) Improved partial fingerprint matching based on score level fusion using pore and sift features. Proceeding of the IEEE International conference on process automation control and computing. 1–4. Coimbatore

  25. Maltoni D, Maio D, Prabhakar S, Jain AK (2009) Handbook of fingerprint recognition. Springer Science and Business Media, London

    Book  Google Scholar 

  26. Manogaran G, Lopez D (2016) Health data analytics using scalable logistic regression with stochastic gradient descent. Int J Adv Intell Paradig 9:1–15

    Google Scholar 

  27. Manogaran G, Lopez D (2017) Disease surveillance system for big climate data processing and dengue transmission. Int J Ambient Comput Intell 8(2):1–25

    Article  Google Scholar 

  28. Manogaran G, Lopez D (2017) Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.04.006

  29. Manogaran, G., & Lopez, D. (2017). A Gaussian process based big data processing framework in cluster computing environment. Clust Comput 1–16. doi:https://doi.org/10.1007/s10586-017-0982-5

  30. Manogaran G, Lopez D (2017) A survey of big data architectures and machine learning algorithms in healthcare. Int J Biomed Eng Technol 25(2–4):182–211

    Article  Google Scholar 

  31. Manogaran, G., Varatharajan, R., & Priyan, M. K. (2017). Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimed Tools Appl 1–21. doi:https://doi.org/10.1007/s11042-017-5515-y

  32. Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2017) A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2017.10.045

  33. Manogaran, G., Lopez, D., Thota, C., Abbas, K. M., Pyne, S., & Sundarasekar, R. (2017a). Big data analytics in healthcare internet of things. In Innovative healthcare systems for the 21st century. Springer International Publishing, Berlin, pp. 263–284

  34. Manogaran, G., Thota, C., Lopez, D., Vijayakumar, V., Abbas, K. M., & Sundarsekar, R. (2017b). Big data knowledge system in healthcare. In Internet of things and big data technologies for next generation healthcare. Springer International Publishing, Berlin, p. 133–157

  35. Manogaran, G., Thota, C., & Lopez, D. (2018). Human-computer interaction with big data analytics. In HCI challenges and privacy preservation in big data security. IGI Global, India, pp. 1–22

  36. Manogaran, G., Vijayakumar, V., Varatharajan, R., Kumar, P. M., Sundarasekar, R., & Hsu, C. H. Machine learning based big data processing framework for cancer diagnosis using hidden markov model and GM clustering. Wirel Pers Commun, 1–18. doi:https://doi.org/10.1007/s11277-017-5044-z

  37. Mao K, Zhu Z, Jiang H (2010) A fast fingerprint image enhancement method. Proceedings of the IEEE third international joint conference on computational science and optimization. 1, 222–226. China

  38. Park U, Pankanti S, Jain AK (2008) Fingerprint verification using SIFT features. Proceedings of the international society for optics and photonics in SPIE defense and security symposium. 69440K–69440K. Orlando

  39. Paulino AA, Feng J, Jain AK (2013) Latent fingerprint matching using descriptor-based houghtransform. IEEE Trans Inf Forensics Secur 8(1):31–45

    Article  Google Scholar 

  40. Selvi M, George A (2013) FBFET: fuzzy based fingerprint enhancement technique based on adaptive thresholding. Proceedings of the IEEE fourth international conference on computing. Communications and networking technologies. 1–5. Tiruchengode

  41. Sherlock BG, Monro DM, Millard K (1994) Fingerprint enhancement by directional Fourier filtering. IET proceedings vision. Image Signal Process 141(2):87–94

    Article  Google Scholar 

  42. Skrypnyk I, Lowe DG (2004) Scene modeling, recognition and tracking with invariant image features. Proceeding of the third IEEE and ACM international symposium on mixed and augmented reality. 110–119. USA

  43. Thota, C., Sundarasekar, R., Manogaran, G., Varatharajan, R., & Priyan, M. K. (2018). Centralized fog computing security platform for IoT and cloud in healthcare system. In Exploring the convergence of big data and the internet of things. IGI Global, Hershey, pp. 141–154

  44. Varatharajan, R., Manogaran, G., & Priyan, M. K. (2017). A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimed Tools Appl 1–21. doi:https://doi.org/10.1007/s11042-017-5318-1

  45. Varatharajan R, Manogaran G, Priyan MK, Balaş VE, Barna C (2017a) Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimed Tools Appl 1–21. doi: https://doi.org/10.1007/s11042-017-4768-9

  46. Varatharajan R, Vasanth K, Gunasekaran M, Priyan M, Gao XZ (2017b) An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.05.035

  47. Wu C, Shi Z, Govindaraju V (2004) Fingerprint image enhancement method using directional median filter. Proceedings of the International society for optics and photonics in SPIE Defense and security symposium. 66–75. Orlando

  48. Yang Y, Liu W, Zhang L (2010) Study on improved scale invariant feature transform matching algorithm. Proceeding of the second pacific-asia conference on circuits. communications and system. 1:398–401. China

  49. Yoon S, Feng J, Jain AK (2011) Latent fingerprint enhancement via robust orientation field estimation. Proceeding of the IEEE international joint conference on biometrics. 1–8. Washington

  50. Yoon S, Cao K, Liu E, Jain AK (2013) LFIQ: latent fingerprint image quality. Proceeding of the IEEE sixth international conference on theory. Applications and systems. 1–8. Arlington

  51. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

Download references

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Correspondence to Malarvizhi Kumar Priyan.

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Manickam, A., Devarasan, E., Manogaran, G. et al. RETRACTED ARTICLE: Score level based latent fingerprint enhancement and matching using SIFT feature. Multimed Tools Appl 78, 3065–3085 (2019). https://doi.org/10.1007/s11042-018-5633-1

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  • DOI: https://doi.org/10.1007/s11042-018-5633-1

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