Implementing Deep Learning: A Novel Approach in CNNs for Face Recognition

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Zubin C. Bhaidasna, Priya R. Swaminarayan, Hetal Z. Bhaidasna

10.14445/23488379/IJEEE-V11I8P126

How to Cite?

Zubin C. Bhaidasna, Priya R. Swaminarayan, Hetal Z. Bhaidasna, "Implementing Deep Learning: A Novel Approach in CNNs for Face Recognition," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 8, pp. 295-308, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I8P126

Abstract:

Considering the vast amount of data available, it is clear that facial recognition and related technologies have made great progress in recent years. Facial recognition is especially important for law enforcement and forensic science in verifying someone's identity. Many researchers are working on using deep learning and machine learning to identify and classify people accurately based on their facial features. The first part of this review focuses on deep learning methods for facial identification and matching. The second part looks at a new technique that improves the accuracy of facial recognition algorithms by using large datasets for training. The paper discusses different ways to identify facial features from images and videos and proposes methods to improve accuracy, achieving 99.50% and 96.75% on the LFW and YTF datasets, respectively.

Keywords:

Deep Learning, CNN, Face recognition, LetNet, AlexNet, ZFNet, Google Net, ResNet, R CNN, YOLO.

References:

1] Kanggeon Kim et al., “Face and Body Association for Video-Based Face Recognition,” 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, pp. 39-48, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Nate Crosswhite et al., “Template Adaptation for Face Verification and Identification,” Image and Vision Computing, vol. 79, pp. 35-48, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Carolina Todedo Ferraz, and Jose Hiroki Saito, “A Comprehensive Analysis of Local Binary Convolution Neural Network for Fast Face Recognition in Surveillance Video,” WebMedia '18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, pp. 265-268, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Jun-Cheng Chen et al., “Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks,” International Journal of Computer Vision, vol. 126, pp. 272-291, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Steve Lawrence et al., “Face Recognition: A Convolutional Neural-Network Approach,” IEEE Transactions on Neural Networks, vol. 8, no. 1, pp. 98-113, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Peng Lu, Baoye Song, and Lin Xu, “Human Face Recognition Based on Convolutional Neural Network and Augmented Dataset,” Systems Science & Control Engineering, vol. 9, no. 2, pp. 29-37, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Jiankang Deng et al., “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4690-4699, 2019.
[Google Scholar] [Publisher Link]
[8] Weiyang Liu et al., “SphereFace: Deep Hypersphere Embedding for Face Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 212-220, 2017.
[Google Scholar] [Publisher Link]
[9] Florian Schroff, Dmitry Kalenichenko, and James Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815-823, 2015. [Google Scholar] [Publisher Link]
[10] Hao Wang et al., “Cosface: Large Margin Cosine Loss for Deep Face Recognition,” Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5265-5274, 2018.
[Google Scholar] [Publisher Link]
[11] Ran He et al., “Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 7, pp. 1761-1773, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Yibo Ju et al., “Adversarial Embedding and Variational Aggregation for Video Face Recognition,” 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), Redondo Beach, CA, USA, pp. 1-8, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Iacopo Masi et al., “Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild,” IEEE Transaction Pattern Analysis Machine Intelligence, vol. 41, no. 2, pp. 379-393, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[14] X. Wang et al., “Deep Discriminative Feature Learning for Face Verification,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[15] Yaniv Taigman et al., “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1701-1708, 2014.
[Google Scholar] [Publisher Link]
[16] Zhen Dong et al., “Deep CNN Based Binary Hash Video Representations for Face Retrieval,” Pattern Recognition, vol. 81, pp. 357-369, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Zhiwu Huang et al., “Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 12, pp. 2827-2840, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ahmed Jawad A. AlBdairi, Zhu Xiao, and Mohammed Alghaili, “Identifying Ethnics of People through Face Recognition: A Deep CNN Approach,” Scientific Programming, vol. 2020, no. 1, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Asifullah Khan et al., “A Survey of the Recent Architectures of Deep Convolutional Neural Networks,” Artificial Intelligence Review, vol. 53, pp. 5455-5516, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Shilpi Singh, and S.V.A.V. Prasad, “Techniques and Challenges of Face Recognition: A Critical Review,” Procedia Computer Science, vol. 143, pp. 536-543, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] C. Liu et al., “Semantic Face Parsing with Occlusion Aware Network,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[22] X. Wang et al., “Deep Discriminative Feature Learning for Face Verification,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[23] R. Zhang, J. Tang, and J. Zhang, “Deep Mutual Learning,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
[24] H. Kharroubi, E. Granger, and A. Hadid, “Deep Learning for Face Recognition: A Comprehensive Review,” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2130-2134, 2017.
[25] Yi Sun et al., “Deep Learning Face Representation by Joint Identification-Verification,” Advances in Neural Information Processing Systems (NIPS), pp. 1-9, 2014.
[Google Scholar] [Publisher Link]
[26] Zeynep Batmaz et al., “A Review on Deep Learning for Recommender Systems: Challenges and Remedies,” Artificial Intelligence Review, vol. 52, pp. 1-37, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Jiuxiang Gu et al., “Recent Advances in Convolutional Neural Networks,” Pattern Recognition, vol. 77, pp. 354-377, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Ahmed A. Elngar et al., “Image Classification Based on CNN: A Survey,” Journal of Cybersecurity and Information Management, vol. 6, no. 1, pp. 18-50, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Sakshi Indolia et al., “Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach,” Procedia Computer Science, vol. 132, pp. 679-688, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Shrey Srivastava et al., “Comparative Analysis of Deep Learning Image Detection Algorithms,” Journal of Big Data, vol. 8, no. 66, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Mohammed Alghaili, Zhiyong Li, and Hamdi A.R. Ali, “Facefilter: Face Identification with Deep Learning and Filter Algorithm,” Scientific Programming, vol. 2020, no. 1, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Wanglong Wu et al., “Recursive Spatial Transformer (REST) for Alignment-Free Face Recognition,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3772-3780, 2017.
[Google Scholar] [Publisher Link]