Design And Development of Sketch Based Image Retrieval Using Deep Learning

Authors

  • Dr S. A. Talekar Department of Information Technology, Savitribai Phule Pune University(SPPU), Pune, Maharashtra India
  • Shravani A. Lajurkar Department of Information Technology, Savitribai Phule Pune University(SPPU), Pune, Maharashtra India
  • Divya S. Patil Department of Information Technology, Savitribai Phule Pune University(SPPU), Pune, Maharashtra India
  • Rutika A. Benke Department of Information Technology, Savitribai Phule Pune University(SPPU), Pune, Maharashtra India
  • Pranjal A. Kunde Department of Information Technology, Savitribai Phule Pune University(SPPU), Pune, Maharashtra India

Keywords:

Forensic Face Sketch, Face Sketch Construction, Face Recognition, Criminal Identification, Deep Learning, Machine Locking.

Abstract

In this cutting edge, the common wrong doing rate is expanding day-by-day and to manage up with this the criminal divisions as well ought to discover ways in which would speed up the by and large preparation and offer assistance in bringing one to justice. In response to rising crime rates, law enforcement agencies are turning to advanced algorithms capable of matching freehand sketches with images in databases. These algorithms, utilizing sophisticated feature extraction techniques and deep learning models, significantly enhance identification accuracy. By leveraging Sketch based image retrieval technology, investigations are expedited, leading to quicker suspect apprehensions and resolution of criminal cases. This results in improved public safety and justice outcomes, as well as more efficient law enforcement practices overall.

 

References

Hamed Kiani Galoogahi and Terence Sim, “Face Sketch Recognition By Local Radon Binary Pattern: LRBP”, 19th IEEE International Conference on Image Processing, 2012.

Charlie Frowd, Anna Petkovic, Kamran Nawaz and Yasmeen Bashir, “Automating the Processes Involved in Facial Composite Production and Identification” Symposium on Bio-inspired Learning and Intelligent Systems for Security, 2009.

W. Zhang, X. Wang and X. Tang, “Coupled information theoretic encoding for face photo-sketch recognition”, in Proc. of CVPR, pp. 513-520, 2011.

X. Tang and X. Wang, “Face sketch synthesis and recognition”, in Proc. of ECCV, pp. 687-694, 2003.

X. Tang and X. Wang, “Face sketch recognition”, IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 50-57, 2004.

B. Klare and A. Jain, “Sketch to photo matching: a feature based approach”, SPIE Conference on Biometric Technology for Human Identification, 2010.

Q. Liu, X. Tang, H. Jin, H. Lu, and S. Ma, “A nonlinear approach for face sketch synthesis and recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1005–1010, June 2005.

P. Yuen and C. Man, “Human face image searching system using sketches,” IEEE Trans. SMC, Part A: Systems and Humans, vol. 37, pp. 493–504, July 2007.

H. Han, B. Klare, K. Bonnen, and A. Jain, “Matching composite sketches to face photos: A component-based approach,” IEEE Trans. on Information Forensics and Security, vol.8, pp. 191–204, January 2013.

P. Jeffrey Brantingham, G. E. Tita, and G. Mohler, “Gang?related crime in Los Angeles remained stable following Covid?19 social distancing orders,” Criminology and Public Policy, vol. 20, no. 3, pp. 423–436, 2021.

K. Yang et al., “Benchmarking commercial emotion detection systems using realistic distortions of facial image datasets,” The Visual Computer, vol. 37, no. 6, pp. 1447-1466, 2021.

L. Capozzi, J. R. Pinto, J. S. Cardoso, and A. Rebelo, “End-to-End Deep Sketch-to-Photo Matching Enforcing Realistic Photo Generation,” in Proc. of the Iberoamerican Congress on Pattern Recognition, 2021, pp 451–460.

S. P. Singh and P. Singh, “An Integrated AFS-Based SWOT Analysis Approach for Evaluation of Strategies Under MCDM Environment”, Journal of Operations and Strategic Planning, vol 1, no 2, pp 129–147, 2018.

S. P. Singh, M. K. Chauhan, and P. Singh, “Using multicriteria futuristic fuzzy decision hierarchy in SWOT analysis: an application in tourism industry,” International Journal of Operations Research and Information Systems, vol. 6, no. 4, pp. 38–56, 2015.

S. P. Singh and P. Singh, “A hybrid decision support model using axiomatic fuzzy set theory in AHP and TOPSIS for multicriteria route selection,” Complex & Intelligent Systems, vol. 4, no. 2, pp. 133–143, 2018.

K. Ounachad, M. Oualla, A. Souhar, and A. Sadiq, “Face sketch recognition-an overview,” in Proc. of the 3rd International Conference on Networking, Information Systems & Security, 2020, pp 1–8.

R. Hopman and A. M’charek, “Facing the unknown suspect: Forensic DNA phenotyping and the oscillation between the individual and the collective,” BioSocieties, vol 15, no 3, pp 438–462, 2020.

M. A. Khan and A. S. Jalal, “Suspect Identification using Local Facial Attributed by Fusing Facial Landmarks on the Forensic Sketch,” in Proc. of the International Conference on Contemporary Computing and Applications (IC3A), 2020, pp 181–186.

M. A. Khan and A. S. Jalal, “A framework for suspect face retrieval using linguistic descriptions,”, xpert Systems with Applications, vol 141, 2020.

Downloads

Published

2024-05-01

How to Cite

[1]
D. S. A. Talekar, S. A. Lajurkar, D. S. Patil, R. A. Benke, and P. A. Kunde, “Design And Development of Sketch Based Image Retrieval Using Deep Learning”, IJIRCST, vol. 12, no. 3, pp. 11–16, May 2024.

Issue

Section

Articles

Similar Articles

1 2 3 4 5 6 > >> 

You may also start an advanced similarity search for this article.