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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64735
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
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.

Computer engineering
Sinhgad Academy of Engineering

Computer engineering
Sinhgad Academy of Engineering
Computer engineering
Sinhgad Academy of Engineering

Computer engineering
Sinhgad Academy of Engineering

Computer engineering
Sinhgad Academy of Engineering
Computer engineering
Sinhgad Academy of Engineering
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
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.
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Publication Date
6/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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