One-Shot Learning for Face Recognition Using Deep Learning: A Survey

Authors

  • Arkan Mahmood Albayati, Wael Chtourou, Faouzi Zarai

Keywords:

Deep learning, one-shot learning, Deep Neural Networks, , DeepID, face recognition.

Abstract

Deep learning uses many layers to express data at different levels in order to extract the data that is required. The DeepID and Deepface penetration rates were stopped in 2014 thanks to the employment of this sort of technology, which expanded and constituted an increasingly important field for study attention. This method stands out due to the hierarchy influence that is created by combining pixels in order that comprise the face images. Enhanced accomplishment has consequently enhanced greatly, which has been an important factor in the accomplishment of presentations on a international scale. Scholars attempted to create one-shot algorithms using deep learning in an attempt to imitate the recognition of faces, an unique situation that separates humanity from other species. Despite deep learning's superior precision as well as methods for identifying diverse image difficulties, the aforementioned methods usually function optimally as a large number of practice examples are accessible. In the present article, we cover a number of research investigations and initiatives that have been completed undertaken in the discipline, as well as an overview of recent advances in recognition of faces and deep one-shot recognition of faces.

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References

S. Liao, A. K. Jain, and S. Z. Li, “Partial Face Recognition: Alignment-Free Approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1193–1205, May 2013, doi: https://doi.org/10.1109/tpami.2012.191.

Y. Ouerhani, M. Jridi, and A. AlFalou, “Fast face recognition approach using a graphical processing unit ‘GPU,’” 2010 IEEE International Conference on Imaging Systems and Techniques, Jul. 2010, doi: https://doi.org/10.1109/ist.2010.5548545.

W. Yang, S. Wang, J. Hu, G. Zheng, and C. Valli, “A fingerprint and finger-vein based cancelable multi-biometric system,” Pattern Recognition, vol. 78, pp. 242–251, Jun. 2018, doi: https://doi.org/10.1016/j.patcog.2018.01.026.

N. P. Patel and A. Kale, “Optimize Approach to Voice Recognition Using IoT,” 2018 International Conference On Advances in Communication and Computing Technology (ICACCT), Feb. 2018, doi: https://doi.org/10.1109/icacct.2018.8529622.

M. Kumar and T. Hussaini, “Face Recognition Algorithm based on Traditional and Artificial Intelligence: A Systematic Review,” IEEE Xplore, Jun. 01, 2021. https://ieeexplore.ieee.org/abstract/document/9498476

Q. Wang, A. Alfalou, and C. Brosseau, “New perspectives in face correlation research: a tutorial,” Advances in Optics and Photonics, vol. 9, no. 1, p. 1, Jan. 2017, doi: https://doi.org/10.1364/aop.9.000001.

C. Zhao, X. Li, and Y. Cang, “Bisecting k-means clustering based face recognition using block-based bag of words model,” Optik - International Journal for Light and Electron Optics, vol. 126, no. 19, pp. 1761–1766, Oct. 2015, doi: https://doi.org/10.1016/j.ijleo.2015.04.068.

M. Wang and W. Deng, “Deep Face Recognition: A Survey,” Neurocomputing, Nov. 2020, doi: https://doi.org/10.1016/j.neucom.2020.10.081.

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition,” ACM Computing Surveys, vol. 35, no. 4, pp. 399–458, Dec. 2003, doi: https://doi.org/10.1145/954339.954342.

A. Scheenstra, A. Ruifrok, and R. C. Veltkamp, “A Survey of 3D Face Recognition Methods,” Lecture Notes in Computer Science, pp. 891–899, 2005, doi: https://doi.org/10.1007/11527923_93.

X. Zhang and Y. Gao, “Face recognition across pose: A review,” Pattern Recognition, vol. 42, no. 11, pp. 2876–2896, Nov. 2009, doi: https://doi.org/10.1016/j.patcog.2009.04.017.

]R. Jafri and H. R. Arabnia, “A Survey of Face Recognition Techniques,” Journal of Information Processing Systems, vol. 5, no. 2, pp. 41–68, Jun. 2009, doi: https://doi.org/10.3745/jips.2009.5.2.041.

K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Computer Vision and Image Understanding, vol. 101, no. 1, pp. 1–15, Jan. 2006, doi: https://doi.org/10.1016/j.cviu.2005.05.005.

X. Zou, J. Kittler, and K. Messer, “Illumination Invariant Face Recognition: A Survey,” Dec. 2007, doi: https://doi.org/10.1109/btas.2007.4401921.

A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: A survey,” Pattern Recognition Letters, vol. 28, no. 14, pp. 1885–1906, Oct. 2007, doi: https://doi.org/10.1016/j.patrec.2006.12.018.

C. Ding and D. Tao, “A Comprehensive Survey on Pose-Invariant Face Recognition,” ACM Transactions on Intelligent Systems and Technology, vol. 7, no. 3, pp. 1–42, Feb. 2016, doi: https://doi.org/10.1145/2845089.

Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face Recognition Systems: A Survey,” Sensors, vol. 20, no. 2, p. 342, Jan. 2020, doi: https://doi.org/10.3390/s20020342.

A. Elmahmudi and H. Ugail, “Deep face recognition using imperfect facial data,” Future Generation Computer Systems, vol. 99, pp. 213–225, Oct. 2019, doi: https://doi.org/10.1016/j.future.2019.04.025.

M. Almuashi, S. Z. Mohd Hashim, D. Mohamad, M. H. Alkawaz, and A. Ali, “Automated kinship verification and identification through human facial images: a survey,” Multimedia Tools and Applications, vol. 76, no. 1, pp. 265–307, Nov. 2015, doi: https://doi.org/10.1007/s11042-015-3007-5.

N. Kohli, M. Vatsa, R. Singh, A. Noore, and A. Majumdar, “Hierarchical Representation Learning for Kinship Verification,” IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 289–302, Jan. 2017, doi: https://doi.org/10.1109/tip.2016.2609811.

Z. Wei, M. Xu, L. Geng, H. Liu, and H. Yin, “Adversarial Similarity Metric Learning for Kinship Verification,” IEEE Access, vol. 7, pp. 100029–100035, 2019, doi: https://doi.org/10.1109/access.2019.2929939.

S. Chen, Y. Liu, X. Gao, and Z. Han, “MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices,” Biometric Recognition, pp. 428–438, 2018, doi: https://doi.org/10.1007/978-3-319-97909-0_46.

J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” openaccess.thecvf.com, 2019. https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.html

J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” openaccess.thecvf.com, 2019. https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.html

W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “SphereFace: Deep Hypersphere Embedding for Face Recognition,” openaccess.thecvf.com, 2017. https://openaccess.thecvf.com/content_cvpr_2017/html/Liu_SphereFace_Deep_Hypersphere_CVPR_201_paper.html

F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” www.cv-foundation.org, 2015. https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Schroff_FaceNet_A_Unified_2015_CVPR_paper.html

Y. Sun, X. Wang, and X. Tang, “Deeply Learned Face Representations Are Sparse, Selective, and Robust,” www.cv-foundation.org, 2015. https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Sun_Deeply_Learned_Face_2015_CVPR_paper.html

F. Wang, W. Liu, H. Liu, and J. Cheng, “Additive Margin Softmax for Face Verification,” IEEE Signal Processing Letters, vol. 25, no. 7, pp. 926–930, Jul. 2018, doi: https://doi.org/10.1109/LSP.2018.2822810.

X. Wang, S. Wang, S. Zhang, T. Fu, H. Shi, and T. Mei, “Support Vector Guided Softmax Loss for Face Recognition,” arXiv.org, Dec. 29, 2018. https://arxiv.org/abs/1812.11317

X. Wang, S. Zhang, Z. Lei, S. Liu, X. Guo, and S. Z. Li, “Ensemble Soft-Margin Softmax Loss for Image Classification,” arXiv.org, May 10, 2018. https://arxiv.org/abs/1805.03922

X. Wu, R. He, Z. Sun, and T. Tan, “A Light CNN for Deep Face Representation With Noisy Labels,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 11, pp. 2884–2896, Nov. 2018, doi: https://doi.org/10.1109/tifs.2018.2833032.

B. Lahasan, S. Lebai Lutfi, I. Venkat, M. A. Al-Betar, and R. San-Segundo, “Optimized symmetric partial facegraphs for face recognition in adverse conditions,” Information Sciences, vol. 429, pp. 194–214, Mar. 2018, doi: https://doi.org/10.1016/j.ins.2017.11.013.

A. Elmahmudi and H. Ugail, “Deep face recognition using imperfect facial data,” Future Generation Computer Systems, vol. 99, pp. 213–225, Oct. 2019, doi: https://doi.org/10.1016/j.future.2019.04.025.

S. Li and W. Deng, “Deep Facial Expression Recognition: A Survey,” IEEE Transactions on Affective Computing, pp. 1–1, 2020, doi: https://doi.org/10.1109/taffc.2020.2981446.

T. Chen, H. Zhang, and H. Jing, “A Survey of Research on Deep Face Recognition based on Gabor Features,” vol. 7, no. 7, pp. 246–250, Jul. 2021, doi: https://doi.org/10.6919/icje.202107_7(7).0038.

Tupe-Waghmare, P., & Joshi, R. R. (2021). Bibliometric Survey on Multipurpose Face Recognition System using Deep Learning. Library Philosophy and Practice, 2021.

R. Goel, I. Mehmood, and H. Ugail, “A Study of Deep Learning-Based Face Recognition Models for Sibling Identification,” Sensors, vol. 21, no. 15, p. 5068, Jul. 2021, doi: https://doi.org/10.3390/s21155068.

P. S. Prasad, R. Pathak, V. K. Gunjan, and H. V. Ramana Rao, “Deep Learning Based Representation for Face Recognition,” Lecture Notes in Electrical Engineering, pp. 419–424, Aug. 2019, doi: https://doi.org/10.1007/978-981-13-8715-9_50.

Sen, J., Sarkar, B., Hena, M. A., & Rahman, M. H. (2020). Face recognition using deep convolutional network and one-shot learning. SSRG International Journal of Computer Science and Engineering, 7(4), 23-9

A. G. Menezes, J. M. D. da C. Sá, E. Llapa, and C. A. Estombelo-Montesco, “Automatic Attendance Management System based on Deep One-Shot Learning,” IEEE Xplore, Jul. 01, 2020. https://ieeexplore.ieee.org/abstract/document/9145230

M. Mehdipour Ghazi and H. Kemal Ekenel, “A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition,” www.cv-foundation.org, 2016. https://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w4/html/Ghazi_A_Comprehensive_Analysis_CVPR_2016_paper.html

X. Yao and Y. Liu, “Machine Learning,” Search Methodologies, pp. 477–517, Jul. 2013, doi: https://doi.org/10.1007/978-1-4614-6940-7_17.

Willard, J., Jia, X., Xu, S., Steinbach, M., & Kumar, V. (2020). Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919, 1(1), 1-34.

“Machine Learning Fundamentals,” Mobility Patterns, Big Data and Transport Analytics, pp. 9–29, Jan. 2019, doi: https://doi.org/10.1016/B978-0-12-812970-8.00002-6.

J. Kaur, M. A. Khan, M. Iftikhar, M. Imran, and Q. Emad Ul Haq, “Machine Learning Techniques for 5G and Beyond,” IEEE Access, vol. 9, pp. 23472–23488, 2021, doi: https://doi.org/10.1109/access.2021.3051557.

M. E. Morocho-Cayamcela, H. Lee, and W. Lim, “Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions,” IEEE Access, vol. 7, pp. 137184–137206, 2019, doi: https://doi.org/10.1109/access.2019.2942390.

P. C. Sen, M. Hajra, and M. Ghosh, “Supervised Classification Algorithms in Machine Learning: A Survey and Review,” Advances in Intelligent Systems and Computing, pp. 99–111, Jul. 2019, doi: https://doi.org/10.1007/978-981-13-7403-6_11.

Y. Liu, C. Yang, L. Jiang, S. Xie, and Y. Zhang, “Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities,” IEEE Network, vol. 33, no. 2, pp. 111–117, Mar. 2019, doi: https://doi.org/10.1109/mnet.2019.1800254.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. Accessed: Jun. 10, 2023. [Online]. Available: https://books.google.ca/books?hl=en&lr=lang_en&id=omivDQAAQBAJ&oi=fnd&pg=PR5&dq=49.%09Goodfellow

V. Dunjko and H. J. Briegel, “Machine learning & artificial intelligence in the quantum domain: a review of recent progress,” Reports on Progress in Physics, vol. 81, no. 7, p. 074001, Jun. 2018, doi: https://doi.org/10.1088/1361-6633/aab406.

L. Kaiser et al., “Model-Based Reinforcement Learning for Atari,” arXiv.org, 2019. https://arxiv.org/abs/1903.00374

K. Zhang, Z. Yang, and T. Başar, “Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms,” Handbook of Reinforcement Learning and Control, pp. 321–384, 2021, doi: https://doi.org/10.1007/978-3-030-60990-0_12.

“Deep reinforcement learning for power system: An overview,” CSEE Journal of Power and Energy Systems, 2019, doi: https://doi.org/10.17775/cseejpes.2019.00920.

L. Cao and Zhimin, “An Overview of Deep Reinforcement Learning,” Jul. 2019, doi: https://doi.org/10.1145/3351917.3351989.

M. Ilham Rizqyawan et al., “Comparing Performance of Supervised Learning Classifiers by Tuning the Hyperparameter on Face Recognition,” vol. 13, no. 5, pp. 1–13, Oct. 2021, doi: https://doi.org/10.5815/ijisa.2021.

Y. Rao, J. Lu, and J. Zhou, “Attention-Aware Deep Reinforcement Learning for Video Face Recognition,” Oct. 2017, doi: https://doi.org/10.1109/iccv.2017.424.

A. Haque, A. Alahi, and L. Fei-Fei, “Recurrent Attention Models for Depth-Based Person Identification,” www.cv-foundation.org, 2016. https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Haque_Recurrent_Attention_Models_CVPR_2016_paper.html

S. Xinhui et al., “Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer,” Oct. 2020, doi: https://doi.org/10.1145/3394171.3413955.

J.-H. Prinz et al., “Markov models of molecular kinetics: Generation and validation,” The Journal of Chemical Physics, vol. 134, no. 17, p. 174105, May 2011, doi: https://doi.org/10.1063/1.3565032.

D. P. Filev and I. Kolmanovsky, “Generalized Markov Models for Real-Time Modeling of Continuous Systems,” IEEE Transactions on Fuzzy Systems, vol. 22, no. 4, pp. 983–998, Aug. 2014, doi: https://doi.org/10.1109/tfuzz.2013.2279535.

J. Shin, K. Yeon, S.-B. Kim, Myung Hoon Sunwoo, and M. Han, “Comparative Study of Markov Chain With Recurrent Neural Network for Short Term Velocity Prediction Implemented on an Embedded System,” vol. 9, pp. 24755–24767, Jan. 2021, doi: https://doi.org/10.1109/access.2021.3056882.

Lal, M., Kumar, K., Arain, R. H., Maitlo, A., Ruk, S. A., & Shaikh, H. (2018). Study of face recognition techniques: a survey. International Journal of Advanced Computer Science and Applications, 9(6).

A. F. Jahwar and A. M. Abdulazeez, “META-HEURISTIC ALGORITHMS FOR K-MEANS CLUSTERING: A REVIEW,” PalArch’s Journal of Archaeology of Egypt / Egyptology, vol. 17, no. 7, pp. 12002–12020, Nov. 2020, Accessed: Feb. 24, 2023. [Online]. Available: https://archives.palarch.nl/index.php/jae/article/view/4630

K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” Journal of Big Data, vol. 3, no. 1, May 2016, doi: https://doi.org/10.1186/s40537-016-0043-6.

F. Zhuang et al., “A Comprehensive Survey on Transfer Learning,” Proceedings of the IEEE, vol. 109, no. 1, pp. 43–76, Jan. 2021, doi: https://doi.org/10.1109/jproc.2020.3004555.

R. Meena. Prakash, N. Thenmoezhi, and M. Gayathri, “Face Recognition with Convolutional Neural Network and Transfer Learning,” 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Nov. 2019, doi: https://doi.org/10.1109/icssit46314.2019.8987899.

R. S. Kute, V. Vyas, and A. Anuse, “Component-based face recognition under transfer learning for forensic applications,” Information Sciences, vol. 476, pp. 176–191, Feb. 2019, doi: https://doi.org/10.1016/j.ins.2018.10.014.

M. Heidari and Kazim Fouladi-Ghaleh, “Using Siamese Networks with Transfer Learning for Face Recognition on Small-Samples Datasets,” Feb. 2020, doi: https://doi.org/10.1109/mvip49855.2020.9116915.

S. S., J. I. Zong Chen, and S. Shakya, “Survey on Neural Network Architectures with Deep Learning,” Journal of Soft Computing Paradigm, vol. 2, no. 3, pp. 186–194, Jul. 2020, doi: https://doi.org/10.36548/jscp.2020.3.007.

Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep Learning for 3D Point Clouds: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2020, doi: https://doi.org/10.1109/tpami.2020.3005434.

A. M. Ozbayoglu, M. U. Gudelek, and O. B. Sezer, “Deep learning for financial applications : A survey,” Applied Soft Computing, vol. 93, p. 106384, Aug. 2020, doi: https://doi.org/10.1016/j.asoc.2020.106384.

M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, and S. C. H. Hoi, “Deep Learning for Person Re-identification: A Survey and Outlook,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi: https://doi.org/10.1109/tpami.2021.3054775.

A. Shrestha and A. Mahmood, “Review of Deep Learning Algorithms and Architectures,” IEEE Access, vol. 7, pp. 53040–53065, 2019, doi: https://doi.org/10.1109/access.2019.2912200.

Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, Apr. 2016, doi: https://doi.org/10.1016/j.neucom.2015.09.116.

S. Sharma and V. Kumar, “Voxel-based 3D occlusion-invariant face recognition using game theory and simulated annealing,” Multimedia Tools and Applications, vol. 79, no. 35–36, pp. 26517–26547, Jul. 2020, doi: https://doi.org/10.1007/s11042-020-09331-5.

T. Bian and Z.-P. Jiang, “Reinforcement learning for linear continuous-time systems: an incremental learning approach,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 2, pp. 433–440, Mar. 2019, doi: https://doi.org/10.1109/jas.2019.1911390.

N. Soltani, B. Soleimani, and Behrang Barekatain, “Heuristic Algorithms for Task Scheduling in Cloud Computing: A Survey,” vol. 9, no. 8, pp. 16–22, Aug. 2017, doi: https://doi.org/10.5815/ijcnis.2017.08.03.

N. Kokash, “An introduction to heuristic algorithms.” Accessed: Jun. 12, 2023. [Online]. Available: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=8314bf30780871868076775ba62759f1faf8c9f0

Y. Li, “Deep Reinforcement Learning: An Overview,” arXiv (Cornell University), Jan. 2017, doi: https://doi.org/10.48550/arxiv.1701.07274.

K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “Deep Reinforcement Learning: A Brief Survey,” IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 26–38, Nov. 2017, doi: https://doi.org/10.1109/msp.2017.2743240.

]S. Gupta, G. Singal, and D. Garg, “Deep Reinforcement Learning Techniques in Diversified Domains: A Survey,” Archives of Computational Methods in Engineering, Feb. 2021, doi: https://doi.org/10.1007/s11831-021-09552-3.

H. Li and H. Xu, “Deep reinforcement learning for robust emotional classification in facial expression recognition,” Knowledge-Based Systems, vol. 204, p. 106172, Sep. 2020, doi: https://doi.org/10.1016/j.knosys.2020.106172.

L. Cao and Zhimin, “An Overview of Deep Reinforcement Learning,” Jul. 2019, doi: https://doi.org/10.1145/3351917.3351989.

X. Huang, M. Kwiatkowska, S. Wang, and M. Wu, “Safety Verification of Deep Neural Networks,” Computer Aided Verification, pp. 3–29, 2017, doi: https://doi.org/10.1007/978-3-319-63387-9_1.

D. A. Tedjopurnomo, Z. Bao, B. Zheng, F. Choudhury, and A. K. Qin, “A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges,” IEEE Transactions on Knowledge and Data Engineering, pp. 1–1, 2020, doi: https://doi.org/10.1109/tkde.2020.3001195.

Y. Liu, X. Ma, J. Bailey, and F. Lu, “Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks,” Computer Vision – ECCV 2020, pp. 182–199, 2020, doi: https://doi.org/10.1007/978-3-030-58607-2_11.

Y. Li, G. Cao, and W. Cao, “LMDAPNet: A Novel Manifold-Based Deep Learning Network,” Jan. 2020, doi: https://doi.org/10.1109/access.2020.2985128.

C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A Survey on Deep Transfer Learning,” Artificial Neural Networks and Machine Learning – ICANN 2018, pp. 270–279, 2018, doi: https://doi.org/10.1007/978-3-030-01424-7_27.

A. Sufian, A. Ghosh, A. S. Sadiq, and F. Smarandache, “A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic,” Journal of Systems Architecture, vol. 108, p. 101830, Sep. 2020, doi: https://doi.org/10.1016/j.sysarc.2020.101830.

J. Manit, L. Preuße, A. Schweikard, and F. Ernst, “Human forehead recognition: a novel biometric modality based on near-infrared laser backscattering feature image using deep transfer learning,” IET Biometrics, vol. 9, no. 1, pp. 31–37, Jan. 2020, doi: https://doi.org/10.1049/iet-bmt.2019.0015.

A. Balatsoukas-Stimming and C. Studer, “Deep Unfolding for Communications Systems: A Survey and Some New Directions,” IEEE Xplore, Oct. 01, 2019. https://ieeexplore.ieee.org/document/9020494

C. Bertocchi, E. Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, and M. Prato, “Deep unfolding of a proximal interior point method for image restoration,” vol. 36, no. 3, pp. 034005–034005, Feb. 2020, doi: https://doi.org/10.1088/1361-6420/ab460a.

M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, Jan. 1991, doi: https://doi.org/10.1162/jocn.1991.3.1.71.

T. Ahonen, A. Hadid, and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037–2041, Dec. 2006, doi: https://doi.org/10.1109/tpami.2006.244.

Chengjun Liu and H. Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition,” IEEE Transactions on Image Processing, vol. 11, no. 4, pp. 467–476, Apr. 2002, doi: https://doi.org/10.1109/tip.2002.999679.

M. Jridi, T. Napoléon, and A. Alfalou, “One lens optical correlation: application to face recognition,” Applied Optics, vol. 57, no. 9, p. 2087, Mar. 2018, doi: https://doi.org/10.1364/ao.57.002087.

F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” www.cv-foundation.org, 2015. https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Schroff_FaceNet_A_Unified_2015_CVPR_paper.html

G. Guo and N. Zhang, “A survey on deep learning based face recognition,” Computer Vision and Image Understanding, p. 102805, Aug. 2019, doi: https://doi.org/10.1016/j.cviu.2019.102805.

Z. Ma, Y. Ding, B. Li, and X. Yuan, “Deep CNNs with Robust LBP Guiding Pooling for Face Recognition,” Sensors, vol. 18, no. 11, p. 3876, Nov. 2018, doi: https://doi.org/10.3390/s18113876.

J. Koo, S. Cho, N. Baek, M. Kim, and K. Park, “CNN-Based Multimodal Human Recognition in Surveillance Environments,” Sensors, vol. 18, no. 9, p. 3040, Sep. 2018, doi: https://doi.org/10.3390/s18093040.

R. Koshy and A. Mahmood, “Optimizing Deep CNN Architectures for Face Liveness Detection,” Entropy, vol. 21, no. 4, p. 423, Apr. 2019, doi: https://doi.org/10.3390/e21040423.

Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face Recognition Systems: A Survey,” Sensors, vol. 20, no. 2, p. 342, Jan. 2020, doi: https://doi.org/10.3390/s20020342.

U. Scherhag, C. Rathgeb, J. Merkle, and C. Busch, “Deep Face Representations for Differential Morphing Attack Detection,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3625–3639, 2020, doi: https://doi.org/10.1109/tifs.2020.2994750.

V. A, D. Hebbar, V. S. Shekhar, K. N. B. Murthy, and S. Natarajan, “Two Novel Detector-Descriptor Based Approaches for Face Recognition Using SIFT and SURF,” Procedia Computer Science, vol. 70, pp. 185–197, 2015, doi: https://doi.org/10.1016/j.procs.2015.10.070.

T. Napoléon and A. Alfalou, “Pose invariant face recognition: 3D model from single photo,” Optics and Lasers in Engineering, vol. 89, pp. 150–161, Feb. 2017, doi: https://doi.org/10.1016/j.optlaseng.2016.06.019.

H. Yang and X. A. Wang, “Cascade classifier for face detection,” Journal of Algorithms & Computational Technology, vol. 10, no. 3, pp. 187–197, Jun. 2016, doi: https://doi.org/10.1177/1748301816649073.

Yousri Ouerhani, Ayman Alfalou, and C. Brosseau, “Road mark recognition using HOG-SVM and correlation,” Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Aug. 2017, doi: https://doi.org/10.1117/12.2273304.

Shah, J. H., Sharif, M., Raza, M., & Azeem, A. (2013). A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques. Int. Arab J. Inf. Technol., 10(6), 536-545.

Y. Kortli, M. Jridi, A. A. Falou, and M. Atri, “A novel face detection approach using local binary pattern histogram and support vector machine,” 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), Mar. 2018, doi: https://doi.org/10.1109/aset.2018.8379829.

T. Napoléon and A. Alfalou, “Pose invariant face recognition: 3D model from single photo,” Optics and Lasers in Engineering, vol. 89, pp. 150–161, Feb. 2017, doi: https://doi.org/10.1016/j.optlaseng.2016.06.019.

Q. Wang, D. Xiong, A. Alfalou, and C. Brosseau, “Optical image authentication scheme using dual polarization decoding configuration,” Optics and Lasers in Engineering, vol. 112, pp. 151–161, Jan. 2019, doi: https://doi.org/10.1016/j.optlaseng.2018.09.008.

M. Annalakshmi, S. M. M. Roomi, and A. S. Naveedh, “A hybrid technique for gender classification with SLBP and HOG features,” Cluster Computing, Jan. 2018, doi: https://doi.org/10.1007/s10586-017-1585-x.

S. Chen, S. Zhong, B. Xue, X. Li, L. Zhao, and C.-I. Chang, “Iterative Scale-Invariant Feature Transform for Remote Sensing Image Registration,” vol. 59, no. 4, pp. 3244–3265, Apr. 2021, doi: https://doi.org/10.1109/tgrs.2020.3008609.

S. U. Hussain, T. Napoléon, and F. Jurie, “Face Recognition using Local Quantized Patterns,” hal.science, 2012. https://hal.science/hal-00806104/

F. Smach, J. Miteran, M. Atri, J. Dubois, M. Abid, and J.-P. Gauthier, “An FPGA-based accelerator for Fourier Descriptors computing for color object recognition using SVM,” Journal of Real-Time Image Processing, vol. 2, no. 4, pp. 249–258, Dec. 2007, doi: https://doi.org/10.1007/s11554-007-0065-6.

Q. Wang, A. Alfalou, and C. Brosseau, “New perspectives in face correlation research: a tutorial,” Advances in Optics and Photonics, vol. 9, no. 1, p. 1, Jan. 2017, doi: https://doi.org/10.1364/aop.9.000001.

F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” www.cv-foundation.org, 2015. https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Schroff_FaceNet_A_Unified_2015_CVPR_paper.html

Thibault Napoléon and Ayman Alfalou, “Local binary patterns preprocessing for face identification/verification using the VanderLugt correlator,” May 2014, doi: https://doi.org/10.1117/12.2051267.

“Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors,” Journal of Imaging, vol. 3, no. 3, p. 37, Sep. 2017, doi: https://doi.org/10.3390/jimaging3030037.

Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, and W. Liu, “CCNet: Criss-Cross Attention for Semantic Segmentation,” openaccess.thecvf.com, 2019. https://openaccess.thecvf.com/content_ICCV_2019/html/Huang_CCNet_CrissCross_Attention_for_Semantic_Segmentation_ICCV_2019_paper.html

R. Ranjan et al., “Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans,” IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 66–83, Jan. 2018, doi: https://doi.org/10.1109/msp.2017.2764116.

M. Mehdipour Ghazi and H. Kemal Ekenel, “A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition,” www.cv-foundation.org, 2016. https://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w4/html/Ghazi_A_Comprehensive_Analysis_CVPR_2016_paper.html

G. Hu et al., “When Face Recognition Meets With Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition,” www.cv-foundation.org, 2015. https://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/html/Hu_When_Face_Recognition_ICCV_2015_paper.html.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv.org, 2014. https://arxiv.org/abs/1409.1556

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” openaccess.thecvf.com, 2016. https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1, Feb. 2017, doi: https://doi.org/10.1609/aaai.v31i1.11231.

C. Szegedy et al., “Going Deeper With Convolutions,” Cv-foundation.org, pp. 1–9, 2015, Available: https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Szegedy_Going_Deeper_With_2015_CVPR_paper.html

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2012, doi: https://doi.org/10.1145/3065386.

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” proceedings.mlr.press, Jun. 01, 2015. https://proceedings.mlr.press/v37/ioffe15.html

Y. Sun, D. Liang, X. Wang, and X. Tang, “DeepID3: Face Recognition with Very Deep Neural Networks,” arXiv.org, 2015. https://arxiv.org/abs/1502.00873

T.-Y. Lin, A. RoyChowdhury, and S. Maji, “Bilinear CNN Models for Fine-Grained Visual Recognition,” www.cv-foundation.org, 2015. https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Lin_Bilinear_CNN_Models_ICCV_2015_paper.html

S. Bianco, R. Cadene, L. Celona, and P. Napoletano, “Benchmark Analysis of Representative Deep Neural Network Architectures,” IEEE Access, vol. 6, pp. 64270–64277, 2018, doi: https://doi.org/10.1109/access.2018.2877890.

Y. Zhang and W. Deng, “Class-Balanced Training for Deep Face Recognition,” openaccess.thecvf.com, 2020. https://openaccess.thecvf.com/content_CVPRW_2020/html/w48/Zhang_Class-Balanced_Training_for_Deep_Face_Recognition_CVPRW_2020_paper.html.

C. Hazirbas et al., “Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness,” arXiv.org, Nov. 10, 2022. https://arxiv.org/abs/2211.05809.

K. S. Krishnapriya, V. Albiero, K. Vangara, M. C. King, and K. W. Bowyer, “Issues Related to Face Recognition Accuracy Varying Based on Race and Skin Tone,” IEEE Transactions on Technology and Society, vol. 1, no. 1, pp. 8–20, Mar. 2020, doi: https://doi.org/10.1109/tts.2020.2974996.

I. K. Tindall, G. J. Curtis, and V. Locke, “Can anxiety and race interact to influence face-recognition accuracy? A systematic literature review,” PLOS ONE, vol. 16, no. 8, p. e0254477, Aug. 2021, doi: https://doi.org/10.1371/journal.pone.0254477.

S. Anwarul and S. Dahiya, “A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy,” Lecture Notes in Electrical Engineering, vol. 597, pp. 495–514, Nov. 2019, doi: https://doi.org/10.1007/978-3-030-29407-6_36.

S. Z. Li and A. K. Jain, Eds., Handbook of Face Recognition. London: Springer London, 2011. doi: https://doi.org/10.1007/978-0-85729-932-1.

Xiaoguang Lu, A. K. Jain, and D. Colbry, “Matching 2.5D face scans to 3D models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 31–43, Jan. 2006, doi: https://doi.org/10.1109/tpami.2006.15.

V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1063–1074, Sep. 2003, doi: https://doi.org/10.1109/tpami.2003.1227983.

“Face recognition: Face in video, age invariance, and facial marks - ProQuest,” www.proquest.com. https://www.proquest.com/openview/d703425d59577701b3a5b02be98e973c/1?pq-origsite=gscholar&cbl=18750

D. Salama AbdELminaam, A. M. Almansori, M. Taha, and E. Badr, “A deep facial recognition system using computational intelligent algorithms,” PLOS ONE, vol. 15, no. 12, p. e0242269, Dec. 2020, doi: https://doi.org/10.1371/journal.pone.0242269.

V. Khong, Z. Zeng, L. Fang, and S. Wang, “One-Shot Face Recognition Based on Multiple Classifiers Training,” Feb. 2021, doi: https://doi.org/10.1145/3457682.3457748.

Y. Guo and L. Zhang, “One-shot Face Recognition by Promoting Underrepresented Classes,” arXiv.org, Mar. 15, 2018. https://arxiv.org/abs/1707.05574

Hasan, M. M., Uddin, M. J., Ahmed, K. T., Rabbi, M. R., Abdullah-Al-Imran, M., & Saha, S. Design and Develop a One-Shot Learning Face Recognition System using Deep Convolutional Network. doi: https://10.9790/1676-1503011624

A. Sabri and Samsul Setumin, “One-Shot Learning for Facial Sketch Recognition using the Siamese Convolutional Neural Network,” Apr. 2021, doi: https://doi.org/10.1109/iscaie51753.2021.9431773.

Z. Ding, Y. Guo, L. Zhang, and Y. Fu, “Generative One-Shot Face Recognition,” arXiv.org, Sep. 28, 2019. https://arxiv.org/abs/1910.04860

Z. J. Wu and W. Deng, “One-shot deep neural network for pose and illumination normalization face recognition,” Jul. 2016, doi: https://doi.org/10.1109/icme.2016.7552902.

L. Wang, Y. Li, and S. Wang, “Feature Learning for One-Shot Face Recognition,” Oct. 2018, doi: https://doi.org/10.1109/icip.2018.8451464.

S. Chanda, A. C. GV, A. Brun, A. Hast, U. Pal, and D. Doermann, “Face Recognition - A One-Shot Learning Perspective,” 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Nov. 2019, doi: https://doi.org/10.1109/sitis.2019.00029.

Y. Zhang, S. Zhang, Y. He, C. Li, C. C. Loy, and Z. Liu, “One-shot Face Reenactment,” arXiv:1908.03251 [cs, eess], Aug. 2019, Available: https://arxiv.org/abs/1908.03251

S. Xiang et al., “One-Shot Identity-Preserving Portrait Reenactment,” arXiv.org, Apr. 26, 2020. https://arxiv.org/abs/2004.12452

A. Anisa and Samsul Setumin, “The Performance of Siamese Neural Network for Face Recognition using Different Activation Functions,” Mar. 2021, doi: https://doi.org/10.1109/ictsa52017.2021.9406549.

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14.08.2024

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Arkan Mahmood Albayati. (2024). One-Shot Learning for Face Recognition Using Deep Learning: A Survey. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 2473 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/6676

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Vol. 12 No. 4 (2024)

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