Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 3
May-June 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar Wiki for Call for Papers WorldCat Zenodo

Sketch Match-Net: Deep Feature Encoding for Scalable Sketch-to-Photo Sketch Recognition

Author(s) Ajay Rajendra Yadav, Prof. L. H. Patil, Dhiraj Rajbahadur Pal, Rehan Mustaq Sheikh, Mantasha Aspak Khan, Sanika Nitin Gedam
Country India
Abstract Traditional methods of suspect identification from forensic sketches are still highly dependenton human analysis, which results in the process being slow, subjective, and untrustworthy.Artistic talent and human memory are known to impact the accuracy of the sketch-basedmethod of suspect identification. In this context, the need to overcome the challenges arose,leading to the development of this research, which put forth the concept of Sketch Match-Net,an automatic system for improved sketch-photo matching techniques for enhanced suspectidentification using deep learning approaches. The proposed system uses the ConvolutionalNeural Network (CNN) approach to bridge the gap between the presentation of the human-composed sketch and actual facial photographs. An interactive digital sketch builder helpsinvestigators to prepare the composite face using separate facial components, which areaccessed using the intuitive “drag-and-drop tool.” The generated sketches are later analyzedusing the trained CNN model for the extraction of distinctive facial patterns, which are latermatched with the cloud-based criminal image database. After successful matching, the systemaccesses the “suspect’s detailed profile, including records of identities and correspondingimages.” The experimental analysis has shown effective improvements in accuracy, speed,and scalability over the traditional manual techniques for suspect identification practices.
Keywords Convolutional Neural Networks, Sketch-to-Photo Matching, Facial Feature Encoding, Deep Learning, Artificial Intelligence, Image Processing, Criminal Identification, Cloud-Based Systems
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-28
View / Download PDF File

Share this



E-ISSN 3048-7641
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

AIJFR DOI prefix is
10.63363/aijfr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJFR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-79-39-59 editor@aijfr.com