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All issues Volume 81 (2026) ITM Web Conf., 81 (2026) 01014 References
Open Access
Issue
ITM Web Conf.
Volume 81, 2026
International Conference on Emerging Technologies for Multidisciplinary Innovation and Sustainability (ETMIS 2025)
Article Number 01014
Number of page(s) 12
DOI https://doi.org/10.1051/itmconf/20268101014
Published online 23 January 2026
  1. X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," IEEE Trans. image Process., vol. 19, no. 6, pp. 1635-1650, 2010. [Google Scholar]
  2. C. Ding, J. Choi, D. Tao, and L. S. Davis, "Multi-directional multi-level dual-cross patterns for robust face recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 3, pp. 518-531, 2015. [Google Scholar]
  3. M. Günther et al., "The 2013 face recognition evaluation in mobile environment," in 2013 International Conference on Biometrics (ICB), IEEE, 2013, pp. 1-7. [Google Scholar]
  4. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face recognition: A literature survey," ACM Comput. Surv., vol. 35, no. 4, pp. 399-458, 2003. [Google Scholar]
  5. X. Wang, T. X. Han, and S. Yan, "An HOG-LBP human detector with partial occlusion handling," in 2009 IEEE 12th international conference on computer vision, IEEE, 2009, pp. 32-39. [Google Scholar]
  6. K.-C. Lee, J. Ho, and D. J. Kriegman, "Acquiring linear subspaces for face recognition under variable lighting," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 5, pp. 684-698, 2005. [Google Scholar]
  7. X. Jiang, Y. O. Kong, J. Huang, R. Zhao, and Y. Zhang, "Learning from real images to model lighting variations for face images," in Computer Vision-ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part IV 10, Springer, 2008, pp. 284-297. [Google Scholar]
  8. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137-1149, 2016. [Google Scholar]
  9. C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9. [Google Scholar]
  10. Z. Zhu, P. Luo, X. Wang, and X. Tang, "Multi-view perceptron: a deep model for learning face identity and view representations," Adv. Neural Inf. Process. Syst., vol. 27, 2014. [Google Scholar]
  11. M. Kan, S. Shan, and X. Chen, "Multi-view deep network for cross-view classification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4847-4855. [Google Scholar]
  12. Y. Tan, "Application Research on Face Image Evaluation Algorithm of Deep Learning Mobile Terminal for Student Check-In Management," Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/3961910. [Google Scholar]
  13. V. C. R, V. Asha, B. Saju, N. S., T. R. M. Reddy, and S. K. M, "Face Recognition and Identification Using Deep Learning," in 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2023, pp. 1-5. doi: 10.1109/ICAECT57570.2023.10118154. [Google Scholar]
  14. S. S. Farfade, M. Saberian, and L. J. Li, "Multi-view face detection using Deep convolutional neural networks," ICMR 2015 - Proc. 2015 ACM Int. Conf. Multimed. Retr., pp. 643-650, 2015, doi: 10.1145/2671188.2749408. [Google Scholar]
  15. S. Z. Li, R. Chu, S. Liao, and L. Zhang, "Illumination Invariant Face Recognition Using Near-Infrared Images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp. 627-639, 2007, doi: 10.1109/TPAMI.2007.1014. [Google Scholar]
  16. W. Ali, W. Tian, S. U. Din, D. Iradukunda, and A. A. Khan, Classical and modern face recognition approaches: a complete review, vol. 80, no. 3. Multimedia Tools and Applications, 2021. doi: 10.1007/s11042-020-09850-1. [Google Scholar]
  17. S. A. Hussain and A. S. A. Al Balushi, "A real time face emotion classification and recognition using deep learning model," in Journal of physics: Conference series, IOP Publishing, 2020, p. 12087. [Google Scholar]
  18. X. Jiang, D. Zhang, and X. Feng, "Local feature hierarchy for face recognition across pose and illumination," in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), IEEE, 2016, pp. 1-5. [Google Scholar]
  19. S. Manna, S. Ghildiyal, and K. Bhimani, "Face recognition from video using deep learning," in 2020 5th International conference on communication and electronics systems (ICCES), IEEE, 2020, pp. 1101-1106. [Google Scholar]
  20. S.-I. Choi and C.-H. Choi, "An effective face recognition under illumination and pose variations," in 2007 International Joint Conference on Neural Networks, IEEE, 2007, pp. 914-919. [Google Scholar]
  21. C. Wang and Y. Li, "Combine image quality fusion and illumination compensation for video-based face recognition," Neurocomputing, vol. 73, no. 7-9, pp. 1478-1490, 2010. [Google Scholar]
  22. G. Zheng and Y. Xu, "Efficient face detection and tracking in video sequences based on deep learning," Inf. Sci. (Ny)., vol. 568, pp. 265-285, 2021. [Google Scholar]

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