Skip to main content

An Automated Person Authentication System with Photo to Sketch Matching Technique

  • Conference paper
  • First Online:
Intelligent Data Engineering and Analytics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

Abstract

Recently, considerable measures are taken to improve security in various locations. Most of the methods use the image-supported identification due to its accuracy and robustness. Usually, the picture assessment engages in capturing the raw image, identifying the vital information, extracting the features, and classification and authentication. This technique is used widely in human recognition and also in criminal science, recognition of the right individual is necessary for the verification necessities. In these applications, usually, the sketch drawn by an expert or a computer is matched alongside the digital photographs accessible in the criminal or the public database. Throughout this assessment, necessary facial features are mined from the photo and compared it with the original picture. This paper aims to evaluate the existing picture improvement and recognition procedures in the literature. After generating the necessary drawing for an individual, a relative examination against the digital picture of the person is executed and the image similarity measures are computed to authenticate photo with the sketch. This work proposes a methodology to evacuate the proposed technique using benchmark datasets and the result demonstrates a better result.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li, SZ, Jain, AK: Handbook of Face Recognition, NY, USA. Springer (2011)

    Google Scholar 

  2. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)

    Article  Google Scholar 

  3. Medioni, G., Choi, J., Kuo, C.-H., Fidaleo, D.: Identifying non cooperative subjects at a distance using face images and inferred three dimensional face models. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 39(1), 12–24 (2009)

    Article  Google Scholar 

  4. Vijayakumari, V.: Face recognition techniques: a survey. World J. Comput. Appl. Technol. 2, 41–50 (2013)

    Google Scholar 

  5. Fernandes, S.L., Bala, G.J.: Self-similarity descriptor and local descriptor-based composite sketch matching. Adv. Intell. Syst. Comput. 436, 643–649 (2016). https://doi.org/10.1007/978-981-10-0448-3_53

    Article  Google Scholar 

  6. Fernandes, S.L., Bala, G.J.: A study on face recognition under facial expression variation and occlusion. Adv. Intell. Syst. Comput. 397, 371–377 (2016). https://doi.org/10.1007/978-81-322-2671-0_35

    Article  Google Scholar 

  7. Klum, C., Han, H., Klare, B., Jain, A.K.: The FaceSketchID system: matching facial composites to mugshots. IEEE Trans. Inf. Forensics Secur. (TIFS) 9(12), 2248–2263 (2014)

    Article  Google Scholar 

  8. Tang, X., Wang, X.: Face sketch recognition, IEEE Trans. Circuits Syst. Video Technol. (CSVT), Spec. Issue Image Video Based Biometrics 14(1), 50–57 (2004)

    Google Scholar 

  9. Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 31 (2009)

    Google Scholar 

  10. Martinez, A.M., Benavente, R.: The AR Face Database. CVC Technical Report #24 (1998)

    Google Scholar 

  11. CUHK. http://mmlab.ie.cuhk.edu.hk/archive/facesketch.html

  12. Lakshmi, B., Parthasarathy, S.: Human action recognition using median background and max pool convolution with nearest neighbor. Int. J. Ambient Comput. Intell. (IJACI) 10(2), 34–47 (2019). https://doi.org/10.4018/IJACI.2019040103

    Article  Google Scholar 

  13. Wang, R., Wang, G.: Web text categorization based on statistical merging algorithm in big data environment. Int. J. Ambient Comput. Intell. (IJACI) 10(3), 17–32 (2019). https://doi.org/10.4018/IJACI.2019070102

    Article  Google Scholar 

  14. Rajinikanth, V., Dey, N., Satapathy, S.C., Ashour, A.S.: An approach to examine magnetic resonance angiography based on Tsallis entropy and deformable snake model. Futur. Gener. Comput. Syst. 85, 160–172 (2018). https://doi.org/10.1016/j.future.2018.03.025

    Article  Google Scholar 

  15. Raja, N.S.M., Fernandes, S.L. Dey, N., Satapathy, S.C., Rajinikanth, V.: Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. J. Ambient. Intell. Humaniz. Comput. 1–12 (2018). https://doi.org/10.1007/s12652-018-0854-8

  16. Raja, N.S.M., Rajinikanth, V., Latha, K.: Otsu based optimal multilevel image thresholding using firefly algorithm. Model Simul. Eng., 17. Article ID 794574 (2014)

    Google Scholar 

  17. Rajinikanth, V., Couceiro, M.S.: Optimal multilevel image threshold selection using a novel objective function. Inf. Syst. Des. Intell. Appl. Adv. Intell. Syst. Comput. 340, 177–186 (2015)

    Google Scholar 

  18. Ali, et al.: Adam deep learning with SOM for human sentiment classification. Int. J. Ambient Comput. Int. (IJACI) 10(3), 92–116 (2019). https://doi.org/10.4018/IJACI.2019070106

    Article  Google Scholar 

  19. Acharya, U.R., et al.: Automated detection of Alzheimer’s disease using brain MRI images—a study with various feature extraction techniques. J. Med. Syst. 43, 302 (2019). https://doi.org/10.1007/s10916-019-1428-9

    Article  Google Scholar 

  20. Jahmunah, V., et al.: Automated detection of schizophrenia using nonlinear signal processing methods. Artif. Intell. Med. 100, 101698 (2019). https://doi.org/10.1016/j.artmed.2019.07.006

    Article  Google Scholar 

  21. Mehmood, Z., et al.: Content-based image retrieval based on visual words fusion versus features fusion of local and global features. Arabian J. Sci. Eng. 43(12), 7265–7284 (2018). https://doi.org/10.1007/s13369-018-3062-0

    Article  Google Scholar 

  22. Fernandes, S.L., Rajinikanth, V., Kadry, S.: A hybrid framework to evaluate breast abnormality using infrared thermal images. IEEE Consum. Electron. Mag. 8(5), 31–36 (2019). https://doi.org/10.1109/MCE.2019.2923926

    Article  Google Scholar 

  23. Bhandary, A., et al.: Deep-learning framework to detect lung abnormality—a study with chest X-ray and lung CT scan images. Pattern Recogn. Lett. (2019). https://doi.org/10.1016/j.patrec.2019.11.013

    Article  Google Scholar 

  24. Bhateja, V., Misra, M., Urooj, S.: Unsharp masking approaches for HVS based enhancement of mammographic masses: a comparative evaluation. Futur. Gener. Comput. Syst. 82, 176–189 (2018)

    Article  Google Scholar 

  25. Srivastava, A., Bhateja, V., Moin, A.: Combination of PCA and contourlets for multispectral image fusion. Adv. Intell. Syst. Comput. 469, 577–585 (2017). https://doi.org/10.1007/978-981-10-1678-3_55

    Article  Google Scholar 

  26. Rajinikanth, V., Fernandes, S.L., Bhushan, B., Sunder, N.R.: Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. Lecture Notes in Electrical Engineering, vol. 434, pp. 313–321 (2018

    Google Scholar 

  27. Fernandes, S.L., et al.: A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians. Neural Comput. Appl. 1–12 (2019). https://doi.org/10.1007/s00521-019-04369-5

  28. Dey, N., et al.: Social-group-optimization based tumor evaluation tool for clinical brain mri of flair/diffusion-weighted modality. Biocybern. Biomed. Eng. 39(3), 843–856 (2019). https://doi.org/10.1016/j.bbe.2019.07.005

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Sri Madhava Raja .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Resmi, P., Reshika, R., Sri Madhava Raja, N., Arunmozhi, S., Rao, V.S. (2021). An Automated Person Authentication System with Photo to Sketch Matching Technique. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_63

Download citation

Publish with us

Policies and ethics