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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64735

The Detective Vision: Forensic Face Sketch Construction and Recognition

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64735

Volume:

10 |

6 |

Jun 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64735

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Aryan Dhumal

Aryan Dhumal

Computer engineering

Sinhgad Academy of Engineering

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Sarthak Athare

Sarthak Athare

Computer engineering

Sinhgad Academy of Engineering

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AA

Abhay Adapanawar

Computer engineering

Sinhgad Academy of Engineering

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Yash Deo

Yash Deo

Computer engineering

Sinhgad Academy of Engineering

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Atharva Nigade

Atharva Nigade

Computer engineering

Sinhgad Academy of Engineering

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Mentors

ANA

A. N. Adapanawar

Computer engineering

Sinhgad Academy of Engineering

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Abstract

In forensic science, it is seen that hand-drawn face sketches are still very limited and time consuming when it comes to using them with the latest technologies used for recognition and identification of criminals. In this paper, we present a standalone application which would allow users to create composites face sketch of the suspect without the help of forensic artists using drag and drop feature in the application and can automatically match the drawn composite face sketch with the police database much faster and efficiently using deep learning and cloud infrastructure.

KEYWORDS

Forensic Face Sketch Face Sketch Construction Face Recognition Criminal Identification Deep Learning Machine Locking Two Step Verification.

Cite This Article

Aryan Dhumal, Sarthak Athare, Abhay Adapanawar, Yash Deo, Atharva Nigade, A. N. Adapanawar, "The Detective Vision: Forensic Face Sketch Construction and Recognition," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-7, Jun 2026. https://doi.org/10.55041/IJSREM64735

Full Article/Conclusion

The Project ‘Forensic Face Sketch Construction and Recognition’ is been designed, developed and finally tested keeping the real-world scenarios from the very first splash screen to the final screen to fetch data from the records keeping security, privacy and accuracy as the key factor in every scenario.   The platform displayed a tremendous result on Security point of view by blocking the platform use if the MAC Address and IP Address on load didn’t match the credentials associated with the user in the database and later the OTP system proved its ability to restrict the use of previously generated OTP and even generating the new OTP every time the OTP page is reloaded or the user tries to relog in the platform. The platform even showed good accuracy and speed while face sketch construction and recognition process, provided an average accuracy of more than 90% with a confidence level of 100% when tested with various test cases, test scenario and data sets, which means a very good rate according to related studies on this field.

References

[1]   F.-L. Zhang, J. Wang, E. Shechtman, Z.-Y. Zhou, J.-X. Shi, and S. M. Hu, “PlenoPatch: Patch-based plenoptic image manipulation,” IEEE Trans. Vis. Comput. Graph., vol. 23, no. 5, pp. 1561–1573, May2017. [2]      A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90,2017.   [3]   M. Zhu, N. Wang, X. Gao, and J. Li, “Deep graphical feature learning for face sketch synthesis,” in Proc. 26th Int. Joint Conf. Artif. Intell., 2017, pp. 3574–3580. [4]   N. Wang, X. Gao, L. Sun, and J. Li, “Bayesian face sketch synthesis,” IEEE Trans. Image Process., vol. 26, no. 3, pp. 1264–1274, Mar.2017.   [5]    Y. Song, L. Bao, S. He, Q. Yang, and M.-H. Yang, “Stylizing face images via multiple exemplars,” Comput. Vis. Image Understanding, vol. 162, pp. 135–145, 2017.   [6]    Y. J. Huang, W. C. Lin, I. C. Yeh, and T. Y. Lee, “Geometric and textural blending for 3d model stylization,” IEEE Trans. Vis. Comput. Graph., vol. 24, no. 2, pp. 1114–1126, Feb.2018.   [7]   S. S. Lin, C. C. Morace, C. H. Lin, L. F. Hsu, and T. Y. Lee, “Generation of escher arts with dual perception,” IEEE Trans. Vis. Comput. Graph., vol. 24, no. 2, pp. 1103–1113, Feb.2018.   [8]    N. Wang, X. Gao, and J. Li, “Random sampling for fast face sketch synthesis,” Pattern Recognit., vol. 76, pp. 215–227, 2018.   [9]     Bin Sheng, Ping Li, Chenhao Gao, Kwan-Liu Ma, "Deep Neural Representation Guided Face Sketch Synthesis", IEEE Trans. Vis. Comput. Graph., vol. 25, no. 12,   pp.      3216-3230,       Dec.2019

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Publication Date

6/10/2026

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