Ensemble Learning for Face Recognition in Suspect Identification using Cloud Environment
By: Shilpa Chaudhari, Rajarajeswari S and Archana Rane

References
- S.I. Serengil and A. Ozpinar, “LightFace: A Hybrid Deep Face Recognition Framework, ” 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 2020, pp. 23–27; doi: 10.1109/aSYU50717.2020.9259802
- H. Han et al. “Matching Composite Sketches to Face Photos: A Component-based Approach, ” IEEE Transactions on Information Forensics and Security, vol. 8, no. 1, 2012, pp. 191–204; doi: 10.1109/TIFS.2012.2228856
- Y. Zhong et al., “SFace: Sigmoid-Constrained Hype rsphere Loss for Robust Face Recognition, ” IEEE Transactions on Image Processing, vol. 30, 2021, pp. 2587–2598; doi: 10.1109/tip.2020.3048632.
- D. DeepInsight, “deepin-sight/insightface, ” GitHub, 4 Jun. 2021; https://github.com/deepinsight/insightface
- X. Ning et al., “Face Editing Based on Facial Recognition Features, ” IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 2, 2023, pp. 774–783; doi: 10.1109/TCDS.2022.3182650
- J. Xiang and G. Zhu, “Joint Face Detection and Facial Expression Recognition with MTCNN, ” In 2017 4th International Conference on Information Science and Control Engineering (ICISCE), 2017, pp. 424–427; doi: 10.1109/ICISCE.2017.95
- K. Vangara et al., “Characterizing the Variability in Face Recognition Accuracy Relative to Race, ” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019; doi: 10.1109/CVPRW.2019.00281
- J. Deng et al., ‘ArcFace: Additive Angular Margin Loss for Deep Face Recognition, ” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4690–4699; doi: 10.1109/cvpr.2019.00482.
- A.A. Poeloemgam et al., “Web-based Face Detection and Recognition using YOLO and Dlib, ” In 2023 1 7th International Conference on Telecommunication Systems, Services, and Applications (TSSA), 2023, pp. 1–6; doi: 10.1109/TSSA59948.2023.10366984
- Q. Cao, “Vggface2: A Dataset for Recognising Faces across Pose and Age, ” In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) 2018, pp. 67–74; doi: 10.1109/FG.2018.00020
- J. Hu et al., “Rapid Face Detection in Complex Environments based on the Improved RetinaFace, ” In Proceedings of the 4th International Conference on Advanced Information Science and System, 2023, pp. 1–7; doi: 10.1145/3573834.3574552
- W. Wu, H. Peng, and S. Yu, “Yunet: A Tiny Millisecond-Level Face Detector, ” Machine Intelligence Research, vol. 20, no. 5, 2023, pp. 656–665; doi: 10.1007/s11633-023-1423-y
- J.G. Cavazos et al., “Accuracy Comparison Across Face Recognition Algorithms: Where are We on Measuring Race Bias, ” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 1, 2020, pp. 101–111; doi: 10.1109/TBI0M.2020.3027269.
- M.S.S. Bobde and S.V. Deshmukh, “Face Recognition Technology” International Journal of Computer Science and Mobile Computing, vol. 3, no. 10, 2014, pp.192-202.
- Y.Taigman et al., “DeepFace: Closing the Gap to Human-Level Performance in Face Verification, ” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1701–1708; doi: 10.1109/CVPR.2014.220
- F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering, ” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015; doi: 10.1109/cvpr.2015.7298682.
- J. Deng et al., “Retinaface: Single-shot Multi-level Face Localisation in the Wild, ” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 5203–5212; doi: 10.1109/CVPR42600.2020.00525
- S. Solarova et al., “Reconsidering the regulation of Facial Recognition in Public Spaces, ” AI Ethics, vol. 3, 2023, pp. 625–635; doi: 10.1007/s43681-022-00194-0
- M. Mortensen, “Sneaking AI through the Back Door: Constructing the Identity of Capitol Hill Rioters through Social Media Images and Facial Recognition Rechnologies, ” Information, Communication & Society, 2024, pp. 1–17; doi: 10.1080/1369118X.2024.2358164
- D. Utegen and B.Z. Rakhmetov, “Facial Recognition Technology and Ensuring Security of Biometric Data: Comparative Analysis of Legal Regulation Models, ” Journal of Digital Technologies and Law, vol. 1, no. 3, 2023, pp. 825–844; doi: 10.21202/jdtl.2023.36
- S. Gokulakrishnan et al., “An Optimized Facial Recognition Model for Identifying Criminal Activities using Deep Learning Strategy, ” International Journal of Information Technology, vol. 15, no. 7, 2023, pp. 3907–3921; doi: 10.1007/s41870-023-01420-6
- V. Munusamy and S. Senthilkumar, “Face Identification of Suspects Using Sequential-Deep Convolutional Neural Network, ” In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) 2024, pp. 1–3; doi: 10.1109/ACCESS.2024.3523101
- N.K. Sharma et al., “Enhancing Facial Geometry Analysis by DeepFaceLandmark Leveraging ResNet101 and Transfer Learning, ” International Journal of Information Technology, 2024, pp. 1–21; doi: 10.1007/s41870-024-01872-4
- L. Yu et al., “Facial Expression Recognition Based on Improved VGG-face Model and Transfer Learning, ” In Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology, 2023, pp. 1–7; doi: 10.1145/3627341.3630376
- K.L. Sailaja et al., “Facial Detection and Recognition in Drone Imagery Using FaceNet, ” In International Conference on Advances in Distributed Computing and Machine Learning, 2024, pp. 183–197; doi: 10.1007/978-981-97-1841-2_13
- M. Gulhane et al., “Advancing Facial Recognition: Enhanced Model with Improved Deepface Algorithm for Robust Adaptability in Diverse Scenarios, ” In 2023 1 0th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), vol. 10, pp. 1384–1389; doi: 10.1109/UPCON59197.2023.10434721
- S. Srinivas and M.P. Selvan, “E-CNN-FFE: An Enhanced Convolutional Neural Network for Facial Feature Extraction and Its Comparative Analysis with FaceNet, DeepID, and LBPH Methods, ” In International Conference on Data Management, Analytics & Innovation, 2024, pp. 339–354; doi: 10.1007/978-981-97-3245-6_23
- M.A. Altaha et al., “Facial Expression Recognition based on ArcFace Features and TinySiamese Network, ” In 2023 International Conference on Cyberworlds (CW), pp. 24–31; doi: 10.35784/jcsi.7973
Language: English
Page range: 85 - 94
Submitted on: Aug 21, 2024
Accepted on: Sep 24, 2024
Published on: Jun 22, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2026 Shilpa Chaudhari, Rajarajeswari S, Archana Rane, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.