2D Face Recognition Methodologies and Applications: A review
DOI:
https://doi.org/10.112222/ijits.v1.i1.18064Keywords:
2D face recognition, Face identification applications, Face analytics, Feature extractionAbstract
Face recognition is one of the most dynamic exploration fields of pattern recognition, with numerous uses and applications including distinguishing proof, forensics, access control, legal sciences, and human-computer interactions. Although, distinguishing a face in a group brings up difficult issues about individual opportunities and stances moral issues. 2D methodologies arrived at some level of development and revealed exceptionally high paces of recognition. This presentation is accomplished in controlled conditions where the securing boundaries are controlled, like lighting, point of view, and distance between the camera–subject, occlusions, and masked faces. This work presents an overview of the present status of-the-art methodologies used to improve the classification performance of face recognition process, feature extraction, and face identification limitations and applications.
References
S. M. Bah, F. Ming, “An improved face recognition algorithm and its application in attendance management system”, Array 5. 2020. Doi: https://doi.org/10.1016/j.array.2019.100014
S. Nasr and K. Bouallegue and M. Shoaib and H. Mekki, “Face recognition system using bag of features and multi-class SVM for robot applications”, International Conference on Control, Automation and Diagnosis (ICCAD), pp. 263-268, 2017.
I. Adjabi, A. Ouahabi , A. Benzaoui and A. Taleb-Ahme, “Past, Present, and Future of Face Recognition: A Review”, Electronics, Vol. 9, No. 1188. 2020. doi:10.3390/electronics9081188.
A. J. Shepley, Deep “Learning for Face Recognition: A Critical Analysis”, ARXIVLABS IV, VI, Jul. 12, 2019, https://arxiv.org/pdf/1907.12739.
W. Wójcik, K. Gromaszek, and M. Junisbekov, “Face Recognition: Issues, Methods and Alternative Applications, in Face Recognition - Semisupervised Classification”, Subspace Projection and Evaluation Methods. London, United Kingdom: IntechOpen, 2016. Available: https://www.intechopen.com/chapters/51031 doi: 10.5772/62950
Abd_Almuhsen, F. Almudhafer, Zainab A. Khalaf, “Review of Different Combinations of Facial Expression Recognition System”, Journal of Physics: Conference Series 1591, 012020. 2020. doi:10.1088/1742-6596/1591/1/012020
R. S. Ghiass, O. Arandjelovic, A Bendada., and X. Maldague, “Infrared face recognition: A comprehensive review of methodologies and databases”. Pattern Recognition, Vol. 47(9), pp. 2807-2824. 2014.
G. Porter, G. Doran, “An anatomical and photographic technique for forensic facial identification”. Forensic Sci Int. Vol. 114(2), pp. 97-105. 2000. doi: 10.1016/s0379-0738(00)00290-5.
S.Z. Li, A.K. Jain, “Handbook of Face Recognition”, 2nd ed.; Springer Publishing Company: New York, NY, USA, 2011.
S. Abdel minaam D, AM Almansori, M. Taha, E. Badr “A deep facial recognition system using computational intelligent algorithms”. PLoS ONE, Vol. 15(12), e0242269, 2020. https://doi.org/10.1371/journal.pone.0242269
L. C. Paul , A. Al Sumam, “Face Recognition Using Principal Component Analysis Method”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 1, No. 9. 2012.
P. Peng, I. Portugal, P. Alencar, D. Cowan, “A face recognition software framework based on principal component analysis”. PLoS ONE, Vol. 16. No. 7. 2021, https://doi.org/10.1371/journal.
P. M Shah, “Face Detection from Images Using Support Vector Machine”. Master's Projects. 321. 2012. DOI: https://doi.org/10.31979/etd.wg5s-gyqn
[14] P.K. Medapati, P.H.S.T. Murthy, K.P. Sridhar, “LAMSTAR: For IoT-based face recognition system to manage the safety factor in smart cities”. Trans. Emerg. Telecommun. Technol. pp. 1–15. 2019. online:https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.3843?af=R
M. P. Rajath Kumar, R. K. Sravan and K. M. Aishwarya, “Artificial neural networks for face recognition using PCA and BPNN”, TENCON 2015 - 2015 IEEE Region 10 Conference, pp. 1-6, 2015. doi: 10.1109/TENCON.2015.7373165.
P. Sukhija, S. Behal, P. Singh, “Face Recognition System Using Genetic Algorithm”, Procedia Computer Science, Vol. 85, pp. 410-417, 2016. https://doi.org/10.1016/j.procs.2016.05.183.
S. Md. Tareeq, R. Parveen, L. J. Rozario and Md. Al-Amin Bhuiyan, “Robust Face Detection using Genetic Algorithm”, Information Technology Journal, Vol. 6, pp. 142-147. 2007. Doi: 10.3923/itj.2007.142.147
M. P. Panigrahy, N. Kumar, “Face Recognition using Genetic Algorithm and Neural Networks”, International Journal of Computer Applications, Vol. 55, No.4, pp. 0975 – 8887, 2012.
Y. Zhang; F. Zhang; L. Guo, “Face Recognition Based on Principal Component Analysis and Support Vector Machine Algorithms”, IEEE 40th Chinese Control Conference (CCC), 2021. Doi: 10.23919/CCC52363.2021.9550727
M. Kopaczka, K. Acar and D. Merhof. “Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models”. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP, (VISIGRAPP 2016), Vol. 4, pp. 150-158. 2016. DOI: 10.5220/0005716801500158
A. K. Agrawal, Y. N. Singh,” Evaluation of Face Recognition Methods in Unconstrained Environments”, International Conference on Intelligent Computing, Communication & Convergence (ICCC-2015), Procedia Computer Science 48, PP. 644 – 651. 2015.
A. B. Rubin, “A Facial Challenge: Facial Recognition Technology and the Carpenter Doctrine”, 27 RICH. J.L. & TECH., No. 2, 2021.
C. Mejda, A. Elkefi, W. Bellil, and C. B. Amar. “A Survey of 2D Face Recognition Techniques”, Computers, Vol. 5, No. 4: 21, 2016. https://doi.org/10.3390/computers5040021
P. Jain, S. Gupta, K.R. Ramkumar, “Review on Face Recognition by Machine Learning and Deep Learning Approaches”. 6th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 528-534. 2019.
H. Deng, Z. Feng, G. Qian, X. Lv, H. Li, , G. Li, ,”MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss”. Appl. Sci., 11, 7310. 2021. https://doi.org/10.3390/app11167310
R. Z. Khan, , N. A. Ibraheem, “Genetic Shape Fitting for Hand Gesture Modeling and Feature Extraction using Variable Length Chromosome”. British Journal of Science, Vol. 10, No. 1, pp. 1-11. 2013.
N. A. Ibraheem, M. M. Hasan, “Combining Several Substitution Cipher Algorithms using Circular Queue Data Structure”. Baghdad Science Journal, Vol. 17, No. 4, pp. 1320-1327, 2020. doi:10.21123/bsj.2020.17.4.1320.
A. Br¨omme, C. Busch, A. Dantcheva, K. Raja, C. Rathgeb and A. “Uhl (Eds.): BIOSIG”, Lecture Notes in Informatics (LNI), Gesellschaft f¨ur Informatik, Bonn 2020.
D. Deb, L. Best-Rowden and A. K. Jain, “Face Recognition Performance under Aging”, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 548-556, 2017, doi: 10.1109/CVPRW.2017.82
M. Cho, T. Kim, I. -J. Kim, K. Lee and S. Lee, “Relational Deep Feature Learning for Heterogeneous Face Recognition”, IEEE Transactions on Information Forensics and Security, Vol. 16, pp. 376-388, 2021. doi: 10.1109/TIFS.2020.3013186
F. Xiong and Z. You, “Research on Face Image Recognition System Based on Computer Artificial Intelligence Technology”, IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, pp. 1158-1161, 2023, doi: 10.1109/ICETCI57876.2023.10176850.
S. Liu, L. Zhao, C. Qu, “Research on Image Recognition Technology Based on Artificial Intelligence”, Computer Life Vol. 11, No. 1. 2023.
Y. Sun, Z. Ren, W. Zheng, “Research on Face Recognition Algorithm Based on Image Processing”, Computational Intelligence and Neuroscience, 2-4, 2022, doi: 10.1155/2022/9224203
M. Wang, W. Deng, “Deep Face Recognition: A Survey”. ArXiv. 2018. https://doi.org/10.1016/j.neucom.2020.10.081
M. Khalaf, B. N. Dhannoon, “MSRD-Unet: Multiscale Residual Dilated U-Net for Medical Image Segmentation”, Baghdad Science Journal, Vol. 19(6), pp. 1603-1611, 2022, doi: https://dx.doi.org/10.21123/bsj.2022.7559
[36] H. Soliman, A. Saleh, E.n Fathi, “Face Recognition in Mobile Devices”, International Journal of Computer Applications, Vol. 73, No.2. 2013.
E. Ozen, F. Alim, S. B. Okcu, E. Kavakli, C. Cigla, “Real-Time Face Recognition System at the Edge”, Conference SPIE Defence and commercial Sensing, Proceedings, Vol. 13057, 2024, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII; 130570N, https://doi.org/10.1117/12.3013671
M.O. Oloyede, G.P. Hancke, H.C. Myburgh, “A review on face recognition systems: recent approaches and challenges”. Multimed Tools Appl 79, pp. 27891–27922. 2020. https://doi.org/10.1007/s11042-020-09261-2
M. M Hasan, N. A. Ibraheem, “Face Construction and Recognition using Neural Networks”, International Journal of Computer Systems, Vol. 04, No. 06, 2017, Available at http://www.ijcsonline.com
D. Zeng, R. Veldhuis, and L. Spreeuwers, “A survey of face recognition techniques under occlusion”, IET biometrics, Vol. 10, No. 6, pp. 581-606. 2021.
C. Hu, M. Ye, Y. Du, X. Lu, “Vector projection for face recognition”, Computers & Electrical Engineering, Vol. 40 No. 8, pp. 51-65, 2014, https://doi.org/10.1016/j.compeleceng.2014.08.010.
A. T. Hussein Alrawi, A. Mosslah, M. G. Alwan, “Performance Evaluation For Face Database”, Journal of Southwest Jiao tong University, Vol. 55, No. 1. 2020. Doi: 10.35741/issn.0258-2724.55.1.39
C. Mingtsung, Q. Wei, H. Jiaqi, Z. zhuomin, “Research on the application of face recognition system”, Journal of Physics: Conference Series, 1684, 2020. doi:10.1088/1742-6596/1684/1/012126
V. C. R, V. Asha, B. Saju, S. N, T. R. Mrudhula Reddy and S. K. M, “Face Recognition and Identification Using Deep Learning”, 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, pp. 1-5, 2023. doi: 10.1109/ICAECT57570.2023.10118154.
Md. T. H. Fuad, A. A. Fime, D. Sikder, Md. A. R.Iftee, J. Rabbi, M. S. Al-Rakhami, A. Gumaei, O.Sen, M. Fuad, Md. N. Islam, , “Recent Advances in Deep Learning Techniques for Face Recognition”, IEEE Access, Vol. 9, pp. 99112-99142, 2021. doi: 10.1109/ACCESS.2021.3096136.
S. J. Rashid, A. I. Abdullah, M. A. Shihab, “Face Recognition System Based on Gabor Wavelets Transform, Principal Component Analysis and Support Vector Machine”. International Journal on Advanced Science, Engineering and Information Technology, Vol. 10(3), pp. 959–963, 2020. https://doi.org/10.18517/ijaseit.10.3.8247
W. Lei, Y. Lei, B. Li, and K. Jia, “Support Vector Machines Optimisation for Face Recognition Using Sparrow Search Algorithm”, journal Traitement du Signal, 2023.
T. Matsukawa, T. Uemura, T. Yamaguchi, “A survey of face recognition technology and its application to security systems”. Journal of Electrical Engineering & Technology, Vol. 12(1), pp. 72-84. 2017.
V. Arora, T. Afnas, “A short survey on face recognition”. Journal of applied research on industrial engineering, Vol. 8(2), pp. 169-175. 2021.
L. Aofan., et al. “An Improved Principal Component Analysis (PCA) Face Recognition Technique using Modular Approach”. PriMera Scientific Engineering, Vol. 2(4), pp. 23-35. 2023.
T. Karras, T. Aila, S. Laine, J. Lehtinen, “A Style-Based Generator Architecture for Generative Adversarial Networks”. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2019. https://doi.org/10.48550/arXiv.1812.04948
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, , M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. ArXiv. 2020. https://arxiv.org/abs/2010.11929
K. Vougioukas, S. Petridis, M. Pantic, “Realistic Speech-Driven Facial Animation with GANs”. ArXiv. 2019. https://arxiv.org/abs/1906.06337
J. Ho, A. Jain, P. Abbeel, “Denoising Diffusion Probabilistic Models”. ArXiv, 2020. https://arxiv.org/abs/2006.11239
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, R. Ng, “NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis”. ArXiv. 2020. https://arxiv.org/abs/2003.08934
G. B. Huang, M. Mattar, T. Berg, E. Learned-Miller, “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments”, 2008.
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