Enhancing Face Recognition with Deep Learning Architectures: A Comprehensive Review
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Abstract
The progression of information discernment via facial identification and the emergence of innovative frameworks has exhibited remarkable strides in recent years. This phenomenon has been particularly pronounced within the realm of verifying individual credentials, a practice prominently harnessed by law enforcement agencies to advance the field of forensic science. A multitude of scholarly endeavors have been dedicated to the application of deep learning techniques within machine learning models. These endeavors aim to facilitate the extraction of distinctive features and subsequent classification, thereby elevating the precision of unique individual recognition. In the context of this scholarly inquiry, the focal point resides in the exploration of deep learning methodologies tailored for the realm of facial recognition and its subsequent matching processes. This exploration centers on the augmentation of accuracy through the meticulous process of training models with expansive datasets. Within the confines of this research paper, a comprehensive survey is conducted, encompassing an array of diverse strategies utilized in facial recognition. This survey, in turn, delves into the intricacies and challenges that underlie the intricate field of facial recognition within imagery analysis.
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References
Peng Lu, Baoye Song, Lin Xu. “ Human face recognition based on convolutional neural network and augmented dataset.“ Systems Science & Control Engineering, 2020.
Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou “ArcFace: Additive Angular Margin Loss for Deep Face Recognition”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar. Ching-Hui Chen, Vishal M. Patel, Carlos D. Castillo, Rama Chellappa.” Unconstrained Still/Video-Based Face Verification With Deep Convolutional Neural Networks”, Springer. 2017.
Carolina Todedo Ferraz And Jose Hiroki. , “A Comprehensive Analysis Of Local Binary Convolution Neural Network For Fast Face Recognition In Surveillance Video.” ACM. 2018.
Nate Crosswhite, Jeffrey Byrne, Chris Stauffer, Omkar Parkhi, Aiong Cao And Andrew Zisserman, “Template Adaptation For Face Verification And Identification. 12th International Conference On Automatic Face & Gesture Recognition”, IEEE. 2017.
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li And Wei Liu, “Cosface: Large Margin Cosine Loss For Deep Face Recognition. Conference On Computer Vision And Pattern Recognition.” , IEEE. 2018.
Ran He, Xiang Wu, Zhenan Sun And Tieniu Tan. “Wasserstein Cnn: Learning Invariant Features For NIR-VIS Face Recognition.” IEEE. 2017.
Yibo Ju, Lingxiao Song, Bing Yu, Ran He, Zhenan Sun. “Adversarial Embedding And Variational Aggregation For Video Face Recognition”, IEEE. 2018.
S, D. A. (2021). CCT Analysis and Effectiveness in e-Business Environment. International Journal of New Practices in Management and Engineering, 10(01), 16–18. https://doi.org/10.17762/ijnpme.v10i01.97
Wang, X., Lu, Y., Wang, Z., & Feng, J. (2018). Deep discriminative feature learning for face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018).
Kaiming He; Xiangyu Zhang; Shaoqing Ren; Jian Sun. ”Deep Residual Learning for Image Recognition”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.
Florian Schroff; Dmitry Kalenichenko; James Philbin. “FaceNet: A unified embedding for face recognition and clustering.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015.
Yaniv Taigman; Ming Yang; Marc'Aurelio Ranzato; Lior Wolf. “DeepFace: Closing the Gap to Human-Level Performance in Face Verification.” IEEE Conference on Computer Vision and Pattern Recognition. 2014
Mr. Zubin C. Bhaidasna, Dr. Priya R. Swaminarayan. “A SURVEY ON CONVOLUTION NEURAL NETWORK FOR FACE RECOGNITION”, Journal of Data Acquisition and Processing Vol. 38 (2) 2023
Mr. Zubin C. Bhaidasna, Dr. Priya R. Swaminarayan. “A SURVEY ON CONVOLUTION NEURAL NETWORK FOR FACE RECOGNITION”, Journal of Data Acquisition and Processing Vol. 38 (2) 2023.
Peng Lu, Baoye Song, Lin Xu“ Human face recognition based on convolutional neural network and augmented dataset, Systems Science & Control Engineering, 2020.
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
Mr. Zubin C. Bhaidasna, Dr. Priya R. Swaminarayan. “A SURVEY ON CONVOLUTION NEURAL NETWORK FOR FACE RECOGNITION”, Journal of Data Acquisition and Processing Vol. 38 (2) 2023.
Khan, Asifullah et al. “A survey of the recent architectures of deep convolutional neural networks.” Artificial Intelligence Review (2020).
Mr. Zubin C. Bhaidasna, Dr. Priya R. Swaminarayan. “A SURVEY ON CONVOLUTION NEURAL NETWORK FOR FACE RECOGNITION”, Journal of Data Acquisition and Processing Vol. 38 (2) 2023.
https://www.researchgate.net/figure/Block-diagram-of-Faster-R-CNN_fig1_339463390.
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).