Paper Title : Survey on the compatibility of Face Detection and Recognition Algorithms after a Pandemic
ISSN : 2394-2231
Year of Publication : 2021
Authors: Rajkumar Yadav, Kunal Sakhare, Shantanu Choubey, Rutik Pol, Gaurav Mahato, Anil Kumar Gupta
MLA Style: Rajkumar Yadav, Kunal Sakhare, Shantanu Choubey, Rutik Pol, Gaurav Mahato, Anil Kumar Gupta "Survey on the compatibility of Face Detection and Recognition Algorithms after a Pandemic " Volume 8 - Issue 1 January-February , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: Rajkumar Yadav, Kunal Sakhare, Shantanu Choubey, Rutik Pol, Gaurav Mahato, Anil Kumar Gupta "Survey on the compatibility of Face Detection and Recognition Algorithms after a Pandemic " Volume 8 - Issue 1 January-February , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
Face recognition has attracted many researchers knowing the vast amount of services it can provide. Many computer vision algorithms have been worked upon for extracting unique features from facial images to help differentiate between those unique 2D and 3D(among the most recent once) image representations. The research work ranges from hardcoded elements to modern machine learning methodology and deep learning-based feature engineering and extraction, due to the pandemic caused by covid-19 and safety reasons its almost obvious to see any concerned subject crossing by wearing a mask, which hugely affects the performance of such facial feature extracting models. This survey provides a review of different types of strategies used by various state-of-the-arts and the effects of the changing appearance of subjects in the pandemic and post-pandemic era on pre-pandemic algorithms. First, we summarize the algorithms based on the type of strategies used and describe their importance in performance change caused due to the changing appearance of faces in public places. Second, generalize the core problem in changing results for face detection and recognition algorithms which can be categorized into classes.
 M. Z. Khan, S. Harous, S. U. Hassan, M. U. Ghani Khan, R. Iqbal and S. Mumtaz, "Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing," in IEEE Access, vol. 7, pp. 72622-72633, 2019, doi: 10.1109/ACCESS.2019.2918275.  -C. D. Gürkaynak and N. Arica, "A case study on transfer learning in convolutional neural networks," 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, 2018, pp. 1-4, doi: 10.1109/SIU.2018.8404642.  Sermanet, Pierre & Eigen, David & Zhang, Xiang & Mathieu, Michael & Fergus, Rob & Lecun, Yann. (2013). OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. International Conference on Learning Representations (ICLR) (Banff).  Das, Sukhendu. (2016). Domain Adaptation with Soft-margin multiple feature-kernel learning beats Deep Learning for surveillance face recognition.  Bashbaghi, Saman & Granger, Eric & Sabourin, Robert & Parchami, Armin. (2018). Deep Learning Architectures for Face Recognition in Video Surveillance.  Yang, Shuo & Luo, Ping & Loy, Chen Change & Tang, Xiaoou. (2017). Faceness-Net: Face Detection through Deep Facial Part Responses. IEEE Transactions on Pattern Analysis and Machine Intelligence. PP. 10.1109/TPAMI.2017.2738644.  Saez-Trigueros, Daniel et al. "Face Recognition: From Traditional to Deep Learning Methods." ArXiv abs/1811.00116 (2018): n. pag.  L. He, H. Li, Q. Zhang and Z. Sun, "Dynamic Feature Learning for Partial Face Recognition," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 7054-7063, doi: 10.1109/CVPR.2018.00737.  P. Viola and M. J. Jones. Robust real-time face detection. International Journal of Computer Vision (IJCV), 57(2):137-154, 2004.  P. Hu and D. Ramanan, "Finding Tiny Faces," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 1522-1530, doi: 10.1109/CVPR.2017.166.  J. Deng, J. Guo, E. Ververas, I. Kotsia and S. Zafeiriou, "RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 5202-5211, doi: 10.1109/CVPR42600.2020.00525.  Wang, Jianfeng & Yuan, Ye & Yu, Gang. (2017). Face Attention Network: An effective Face Detector for the Occluded Faces.  He, Yonghao et al. "LFFD: A Light and Fast Face Detector for Edge Devices." ArXiv abs/1904.10633 (2019): n. pag.  I. Masi, Y. Wu, T. Hassner and P. Natarajan, "Deep Face Recognition: A Survey," 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018, pp. 471-478, doi: 10.1109/SIBGRAPI.2018.00067.  Wu, Xiang & He, Ran & Sun, Zhenan & Tan, Tieniu. (2018). A Light CNN for Deep Face Representation with Noisy Labels. IEEE Transactions on Information Forensics and Security. 13. 1-1. 10.1109/TIFS.2018.2833032.  Wang, Xiaobo & Wang, Shuo & Chi, Cheng & Zhang, Shifeng & Mei, Tao. (2020). Loss Function Search for Face Recognition.  Shepley, Andrew. (2019). Deep Learning For Face Recognition: A Critical Analysis.  Zhao, Jian & Cheng, Yu & Cheng, Yi & yang, yang & Lan, Haochong & Zhao, Fang & Xiong, Lin & Xu, Yan & Li, Jianshu & Pranata, Sugiri & Shen, Shengmei & Xing, Junliang & Liu, Hengzhu & Yan, Shuicheng & Feng, Jiashi. (2018). Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition. 10.13140/RG.2.2.24847.23204.  Z. Lu, X. Jiang and A. Kot, "Deep Coupled ResNet for Low-Resolution Face Recognition," in IEEE Signal Processing Letters, vol. 25, no. 4, pp. 526-530, April 2018.  S. Ge, J. Li, Q. Ye and Z. Luo, "Detecting Masked Faces in the Wild with LLE-CNNs," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 426-434, doi: 10.1109/CVPR.2017.53.  F. Schroff, D. Kalenichenko, and J. Philbin. "Facenet: A unified embedding for face recognition and clustering", In Proc. CVPR, 2015.  J. Deng, J. Guo, N. Xue and S. Zafeiriou, "ArcFace: Additive Angular Margin Loss for Deep Face Recognition," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 4685-4694. doi: 10.1109/CVPR.2019.00482  Li, C., Yuan, X., Lin, C., Guo, M., Wu, W., Yan, J., and Ouyang, W. Am-lfs: Automl for loss function search. In Proceedings of the IEEE International Conference on Computer Vision, pp. 8410–8419, 2019.  Parama Bagchi, Debotosh Bhattacharjee, and Mita Nasipuri. Robust 3d face recognition in presence of pose and partial occlusions or missing parts. arXiv preprint arXiv:1408.3709, 2014.  Ashwini S Gawali and Ratnadeep R Deshmukh. 3d face recognition using geodesic facial curves to handle expression, occlusion and pose variations. International Journal of Computer Science and Information Technologies, 5(3):4284–4287, 2014.  Walid, Hariri. (2020). Efficient Masked Face Recognition Method during the COVID-19 Pandemic. 10.21203/rs.3.rs-39289/v1.
deep learning, feature engineering and extraction