Investigating the use of the Siamese network for face sketch – photo recognition
Article Sidebar
Main Article Content
Abstract
Nowadays, there is an increase in the number of CCTV everywhere. However, there are still many cases when CCTV cannot capture criminal activities and officers have to rely heavily on the eyewitness report. The eyewitness report is used as a piece of evidence and creates forensic face sketches of the criminals. The accuracy of a forensic face sketch is normally low depending on the memory of the eyewitness and the ability of the eyewitness to explain. Therefore, identifying persons from forensic face sketches is very challenging. Moreover, manually searching for the suspected photo from a large database by using a forensic face sketch as input is rather impossible.
As a result, in this research, we propose an algorithm to match a forensic face sketch to a suspected photo in a database. The model used is the Siamese network with a twin convolution neural network. The performance of this proposed network is investigated by using the dataset from the Chinese University of Hong Kong (CUHK).
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
- Content and information in articles published in NKRAFA Journal of Science and Technology are comment and responsibility of authors of articles directly. Journal editorial do no need to agree or share any responsibility.
- NKRAFA Journal of Science and Technology Articles holds the copyright of the content, pictures, images etc. which published in it. If any person or agency require to reuse all or some part of articles, the permission must be obtained from the NKRAFA Journal of Science and Technology.
References
Priyadarshi, R., Gupta, B., & Anurag, A. (2020). Deployment techniques in wireless sensor networks: a survey, classification, challenges, and future research issues. The Journal of Supercomputing, 76(9), 7333-7373.
Klare, B., Li, Z., & Jain, A. K. (2010). Matching forensic sketches to mug-shot photos. IEEE transactions on pattern analysis and machine intelligence, 33(3), 639-646.
Klare, B. F., & Jain, A. K. (2012). Heterogeneous face recognition using kernel prototype similarities. IEEE transactions on pattern analysis and machine intelligence, 35(6), 1410-1422.
Ouyang, S., Hospedales, T. M., Song, Y. Z., & Li, X. (2016). Forgetmenot: Memory-aware forensic facial sketch matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5571-5579).
Ounachad, K., Oualla, M., Souhar, A., & Sadiq, A. (2020, March). Face sketch recognition-an overview. In Proceedings of the 3rd International Conference on Networking, Information Systems & Security (pp. 1-8).
Bushra, S. N., & Maheswari, K. U. (2021, April). Crime Investigation using DCGAN by Forensic Sketch-to-Face Transformation (STF)-A Review. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1343-1348). IEEE.
Lahlali, S. E., Sadiq, A., & Mbarki, S. (2015). A REVIEW OF FACE SKETCH RECOGNITION SYSTEMS. Journal of Theoretical & Applied Information Technology, 81(2).
Ashwini, B., & Prajakta, K. (2016). A survey of face recognition from sketches. International Journal of Latest Trends in Engineering and Technology, 6(3), 150-158.
Liu, Q., Tang, X., Jin, H., Lu, H., & Ma, S. (2005, June). A nonlinear approach for face sketch synthesis and recognition. In 2005 IEEE Computer Society conference on computer vision and pattern recognition (CVPR'05) (Vol. 1, pp. 1005-1010). IEEE.
Wang, N., Tao, D., Gao, X., Li, X., & Li, J. (2014). A comprehensive survey to face hallucination. International journal of computer vision, 106, 9-30.
Qi, Y., Song, Y. Z., Zhang, H., & Liu, J. (2016, September). Sketch-based image retrieval via siamese convolutional neural network. In 2016 IEEE international conference on image processing (ICIP) (pp. 2460-2464). IEEE.
Fan, L., Liu, H., & Hou, Y. (2019, July). An improved siamese network for face sketch recognition. In 2019 International Conference on Machine Learning and Cybernetics (ICMLC) (pp. 1-7). IEEE.
Fan, L., Sun, X., & Rosin, P. L. (2021, January). Siamese graph convolution network for face sketch recognition: an application using graph structure for face photo-sketch recognition. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 8008-8014). IEEE.
Wang, X., & Tang, X. (2008). Face photo-sketch synthesis and recognition. IEEE transactions on pattern analysis and machine intelligence, 31(11), 1955-1967.
Zhang, W., Wang, X., & Tang, X. (2011, June). Coupled information-theoretic encoding for face photo-sketch recognition. In CVPR 2011 (pp. 513-520). IEEE.
Galea, C., & Farrugia, R. A. (2016, September). A large-scale software-generated face composite sketch database. In 2016 International Conference of the Biometrics Special Interest Group (BIOSIG) (pp. 1-5). IEEE.