FaceNet – A Framework for Age Variation Facial Digital Images
DOI:
https://doi.org/10.4108/eetsis.5198Keywords:
Deep Convolutional Neural Networks, FaceNet,, Face embeddings, Composite Sketch with Age Variations, Adhoc On demand vector, Deep learningAbstract
Automated face recognition plays a vital role in forensics. The most important evidence in the criminal investigation is the facial images captured from the crime scene, as they represent the identity of the people involved in crime. The role of law enforcement agencies is to identify the facial images from the suitable database. This information can be treated as strong evidence for the law enforcement agencies which becomes the most important evidence in global counter-terrorism initiatives. Contour of chin and cheek, distance
between different features and shapes of facial components are some of the parameters considered by the forensic experts for manual facial identification process. This process is time consuming, and it is a tedious job. To address this issue, there is a need for developing an automated face recognition system for forensics. As a result, FaceNet – a framework for age variation facial digital images is discussed in this research work. Experiments are evaluated on CSA dataset with three age variations which provides a recognition accuracy of
86.8% and performs better than the existing algorithms.
References
Gadekallu T.R., Rajput D.S., Reddy M.P., Lakshmanna K., Bhattacharya S., Singh S. and Alazab M. (2020) A Novel PCA – Whale Optimization based Deep Neural Network Model for Classification of Tomato Plant Diseases using GPU, Journal of Real Time Image Processing, pp. 114.
K. Georgy, B. Katarzyna and N. Shchegoleva (2014) Sketch Generation from Photo to Create Test Databases, Przegld Elektrotechniczny, ISSN 0033- 2097, pp. 97-100.
H. Hu, B. Klare, K. Bonnen and A. Kumar Jain (2014) Matching Composite Sketches to Face Photos: A Component Based Approach, IEEE Transactions on Information and Security.
J. K. Scott, H. Hu, B. Klare, and A. Kumar Jain (2014) Face Sketch id Systems - Matching Composite Sketches to Mugshots, IEEE Transactions on Information and Security.
M. Paritosh, J. Aishwarya, G. Gaurav, S. Richa, and V. Mayank (2017) Recognizing Composite Sketches with Digital Face Images Via SSD dictionary, In Proceedings of International Conference on Computer Vision.
A W Shubhangi and R.G. Sunil (2015) Suspect Identification by Matching Composite Sketch with Mug Shot, Research Journal of Engineering and Technology.
E.L. Salah, S. Abdelalim and M. Samir (2015) A Review of Face Sketch Recognition Systems, Journal of Theoretical and Applied Information Technology, Vol. 81(2).
B. Ashwini and K. Prajakta (2016) A Survey of Face Recognition from Sketches, International Journal of Latest Trends in Engineering and Technology, Vol. 6(3).
T. Ujwala, B. Seema and R. Lata (2013) Forensic Sketch Photo Matching using LFDA, International Journal of Soft Computing and Engineering, Vol. 3(4), pp. 22312307.
K. Scott, H. Hu, A. Kumar Jain and B. Klare (2013) Sketch based Face Recognition Forensic vs Composite Sketches. In Proceedings of International Conference on Biometrics.
S L Fernandesa and Josemin G Bala (2015) Developing a Novel Technique to Match Composite Sketches with Images Captured by Unmanned Aerial Vehicle. In Proceedings of International Conference on Information Security and Privacy.
S. Ouyang, T. Hospedales, Yi-ZheSong, Xueming Li Chen Change Loy and Xiaogang Wang (2016) A Survey on Heterogeneous Face Recognition: Sketch.
X Xue, J Xu, and X Mao (2019) Composite Sketch Recognition using Multi-Scale HOG Features and Semantic Attributes, International Conference on Cyberworlds, pp.121-127.
A M Martinez and R Benavente (1998) The AR Face Database, CVC Technical Report.
Cheraghi H. and Lee H. J. (2019) SP-Net: A Novel Framework to Identify Composite Sketch, IEEE Access, Vol.7, pp. 131749131757.
Kan M., Shan S., Zhang H., Lao S., and Chen X. (2012) Multi-View Discriminant Analysis, In proceedings of European Conference on Computer Vision, Springer, pp.808821.
Iranmanesh S , Kazemi H, Soleymani S, Dabouei A. and Nasrabadi N M (2018) Deep Sketch-Photo Face Recognition Assisted By Facial Attributes. In Proceedings of IEEE 9th International Conference on Biometrics Theory, Applications and Systems, pp. 110.
Chugh T., Singh M., Nagpal S., Singh R. and Vatsa M. (2017) Transfer Learning based Evolutionary Algorithm for Composite Face Sketch Recognition, In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops.
Mittal P., Jain A., Goswami G., Singh R. and Vatsa M. (2014) Recognizing Composite Sketches with Digital Face Images Via SSD Dictionary, Proceedings of IEEE International Joint Conference on Biometrics.
Chugh T., Bhatt H.S., Singh R. and Vatsa M. (2017) Matching Age Separated Composite Sketches and Digital Face Images. In proceedings of 6th International Conference on Biometrics: Theory, Applications and Systems, Arlington, USA.
Hiranmoy R. and Bhattacharjee D. (2019) Heterogeneous Face Matching using Robust Binary Pattern of Local Quotient: RBPLQ, Advances in Intelligent Systems and Computing, Vol.883.
Viola P. and Jones M. (2001) Rapid Object Detection using a Boosted Cascade of Simple Features. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, Vol.1, pp. II.
Peng C., Gao X., Wang N. and Li J. (2018) Face Recognition from Multiple Stylistic Sketches: Scenarios, Datasets and Evaluation, Pattern Recognition, Vol. 84, pp. 262272.
Chethana H.T. and T.C. Nagavi (2021) A Heterogeneous Face Recognition Approach for Matching Composite Sketch with Age Variation Digital Images, IEEE Proceedings of Sixth International Conference on Wireless Communications, Signal Processing and
Networking (WiSPNET 2021), pp. 335-339.
Siddharth K.P. and D.R. Kisku (2017) Heterogeneous Face Identification by Fusion of Local Descriptors, In proceedings of 7th IEEE International Conference on Advance Computing.
Zhang M., Wang N., Li Y. and Gao X. (2019) Neural Probabilistic Graphical Model for Face Sketch Synthesis, IEEE Transactions on Neural Networks and Learning Systems, Vol.31, pp.2623-2637.
Chen Z., Yao S., Jia Y. and Liu C. (2018) Face Sketch-Photo Synthesis and Recognition: Dual-Scale Markov Network and Multi-Information Fusion, Journal of Vision Communication, Image Representation, Vol.51, pp.112-121.
J. Huo, Y. Gao, Y. Shi, W. Yang and H. Yin (2018) Heterogeneous Face Recognition by Margin-based Cross-Modality
Metric Learning,IEEE Transactions on Cybernetics, Vol.48(6), pp. 1814-1826.
Khalil-Hani, M., Sung, L.S (2014) A Convolutional Neural Network Approach for Face Verification, International Conference on High Performance Computing & Simulation (HPCS). IEEE, pp. 707714.
Schroff, F., Kalenichenko, D., Philbin, J (2015) FaceNet: A Unified Embedding for Face Recognition and Clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 815-823.
Galea C., and Farrugia R.A. (2017) Forensic Face Photo-Sketch Recognition using a Deep Learning-Based Architecture, IEEE Signal Processing Letters, Vol. 24(11), pp.1586-1590.
Chethana H.T., and T.C. Nagavi (2021) A New Framework for Matching Forensic Composite Sketches with Digital Images, International Journal of Digital Crime and Forensics, Vol. 13(5), pp.119.
Vredeveldt, A. and Tredoux, C., (2022) Composite Communication: How Dissemination of Facial Composites in the Media Affects Police Investigations, Memory, Mind & Media, 1, E10. doi:10.1017/mem.2022.4,2022.
Tian, Y.E., Cropley, V., Maier, A.B (2023) Heterogeneous Aging across Multiple Organ Systems and Prediction of Chronic Disease and Mortality, Nat Med 29, pp.1221-1231.
U. Cheema and S. Moon (2023) Disguised Heterogeneous Face Recognition using Deep Neighborhood Difference Relational Network, Neurocomputing, Vol. 519, pp.445-6, ISSN 0925-2312.
Liu D, Gao X, Peng C, Wang N, Li J (2022) Heterogeneous Face Interpretable Disentangled Representation for Joint Face Recognition and Synthesis, IEEE Trans Neural Netw Learn Syst. vol. 33(10), pp. 56115625.
Chaoyou Fu, Xiang Wu, Yibo Hu, Huaibo Huang, Ran He (2019) Dual Variational Generation for Low Shot Heterogeneous Face Recognition. Advances in Neural Information Processing Systems, 32NeurIPS.
De Freitas Pereira, T., Anjos, A., and Marcel, S. (2019) Heterogeneous Face Recognition Using Domain Specific Units, IEEE Transactions on Information Forensics and Security, 14, pp. 18031816.
T. de Freitas Pereira, A. Anjos and S. Marcel (2019) Heterogeneous Face Recognition Using Domain Specific Units, in IEEE Transactions on Information Forensics and Security, Vol. 14(7), pp. 1803-1816.
Wan Q, Panetta K (2016) A Facial Recognition System for Matching Computerized Composite Sketches to Facial Photos using Human Visual System Algorithms. In 2016 IEEE Symposium on Technologies for Homeland Security (HST), pp.16
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Chethana H.T., Trisiladevi C. Nagavi, Mahesha P., Vinayakumar Ravi, Gururaj H.L.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
