Optimization of facial recognition authentication system using InceptionResNetV1 with Pretrained VGGFACE2
Abstract
Face recognition as a biometric authentication method continues to evolve due to its high security and ease of use. However, training models from scratch faces challenges such as the need for large datasets and high computational resources. This study aims to optimize the face authentication system using the InceptionResNetV1 architecture with a transfer learning approach from the pretrained VGGFace2 model and to compare its performance with CASIA-WebFace. Face detection is conducted using YOLOv8, face embeddings are generated by InceptionResNetV1, and authentication is performed by calculating the Euclidean distance between embeddings. Face data were collected from university students and divided into training and testing datasets. Performance evaluation includes accuracy, precision, recall, F1-score, and the confusion matrix. The results show that the VGGFace2 model achieved an accuracy of 98.75%, a recall of 100%, and an F1-score of 99.26%, with no False Negatives, while CASIA-WebFace achieved an accuracy of 86.25% with a recall of 85.07%. The main contribution of this study is to demonstrate that the use of transfer learning with the pretrained VGGFace2 model can significantly improve the accuracy of face authentication systems and to show its effectiveness for developing systems with limited data and computational resources. This study contributes by highlighting the superiority of the pretrained VGGFace2 model in face authentication systems and emphasizing the effectiveness of transfer learning for implementing accurate systems under resource constraints.
Keywords: Authentication System, InceptionResNetV1, Face Recognition, Transfer Learning, VGGFace2Full Text:
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P. S. B. Bele, G. S. Band, S. S. Kawade, A. D. Udimkar, and R. N. Khandare, “The Role of Biometric and Authentication in Security,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 12, no. November, pp. 1997–1999, 2024, [Online]. Available: https://www.ijraset.com/best-journal/the-role-of-biometric-and-authentication-in-security Doi: https://doi.org/10.22214/ijraset.2024.65520
Divya Khandekar and Shailesh Bendale, “Biometrics Security System,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 1, pp. 219–224, Mar. 2023, doi: 10.48175/IJARSCT-8588. Doi: https://doi.org/10.48175/IJARSCT-8588
D. Tribuana, Hazriani, and A. L. Arda, “Face recognition for smart door security access with convolutional neural network method,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 22, no. 3, pp. 702–710, 2024, doi: 10.12928/TELKOMNIKA.v22i3.25946. Doi: https://doi.org/10.12928/telkomnika.v22i3.25946
M. A. N. U. Ghani et al., “Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies,” Comput. Mater. Contin., vol. 79, no. 2, pp. 2609-2623, 2024, doi: https://doi.org/10.32604/cmc.2024.049611
K. Alhanaee, M. Alhammadi, N. Almenhali, and M. Shatnawi, “Face recognition smart attendance system using deep transfer learning,” Procedia Comput. Sci., vol. 192, pp. 4093–4102, 2021, doi: 10.1016/j.procs.2021.09.184. Doi: https://doi.org/10.1016/j.procs.2021.09.184
M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Comput. Sco. Press, 1991, pp. 586–591. doi: https://doi.org/10.1109/CVPR.1991.139758
P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720, Jul. 1997, doi: https://doi.org/10.1109/34.598228
S. Swapna Rani, “Enhancing Facial Recognition Accuracy in Low-Light Conditions Using Convolutional Neural Networks,” J. Electr. Syst., vol. 20, no. 5s, pp. 2140–2148, 2024, doi: https://doi.org/10.52783/jes.2559
T. Q. Chung, H. C. Huyen, and D. V. Sang, “A Novel Generative Model to Synthesize Face Images for Pose-invariant Face Recognition,” in 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), IEEE, Oct. 2020, pp. 1–6. doi: https://doi.org/10.1109/MAPR49794.2020.9237763
W. Setiawan, “Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus,” J. Simantec, vol. 7, no. 2, pp. 48–53, 2020, doi: https://doi.org/10.21107/simantec.v7i2.6551
T. A. Kadhim, N. S. Zghal, W. Hariri, and D. Ben Aissa, “Face Recognition in Multiple Variations Using Deep Learning and Convolutional Neural Networks,” in 2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), IEEE, May 2022, pp. 305–311. doi: https://doi.org/10.1109/SETIT54465.2022.9875530
J. J. Sundi, H. Kumar, Bharti, and R. Bedi, “Real-Time Facial Expression Recognition Using Convolutional Neural Networks for Adaptive User Interfaces,” in 2024 5th International Conference for Emerging Technology (INCET), IEEE, May 2024, pp. 1–6. doi: https://doi.org/10.1109/INCET61516.2024.10593062
T. M. Fahrudin and I. Z. A. Illah, “SkinMate: Mobile-Based Application for Detecting Multi-Class Skin Diseases Classification Using Pre-Trained MobileNetV2 on CNN Architecture,” in 2023 IEEE 9th Information Technology International Seminar (ITIS), IEEE, Oct. 2023, pp. 1–6. doi: https://doi.org/10.1109/ITIS59651.2023.10420370
R. Liu, J. Jia, Y. Zhou, Y. Zhou, and Y. Liu, “Training Deep Neural Networks with Large-scale Datasets on Sunway High Performance Computer,” in 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), IEEE, Jun. 2022, pp. 466–471. doi: https://doi.org/10.1109/ICAICA54878.2022.9844581
T. Li, B. He, and Y. Zheng, “Research and Implementation of High Computational Power for Training and Inference of Convolutional Neural Networks,” Appl. Sci., vol. 13, no. 2, 2023, doi: https://doi.org/10.3390/app13021003
S. Ansari, “Computer Vision Modeling on the Cloud,” in Building Computer Vision Applications Using Artificial Neural Networks, Berkeley, CA: Apress, 2023, pp. 457–512. doi: https://doi.org/10.1007/978-1-4842-9866-4_10
W. S. J. Saputra, E. Y. Puspaningrum, W. F. Syahputra, A. P. Sari, Y. V. Via, and M. Idhom, “Car Classification Based on Image Using Transfer Learning Convolutional Neural Network,” in 2022 IEEE 8th Information Technology International Seminar (ITIS), IEEE, Oct. 2022, pp. 324–327. doi: https://doi.org/10.1109/ITIS57155.2022.10010073
A. H. Ali, M. G. Yaseen, M. Aljanabi, S. A. Abed, and C. GPT, “Transfer Learning: A New Promising Techniques,” Mesopotamian J. Big Data, no. February, pp. 29–30, 2023, doi: https://doi.org/10.58496/MJBD/2023/004
L. Yu, N. Jiang, H. Xu, and Z. Qi, “Facial Expression Recognition Based on Improved VGG-face Model and Transfer Learning,” in Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology, New York, NY, USA: ACM, Aug. 2023, pp. 1–7. doi: https://doi.org/10.1145/3627341.3630376
M. Usgan, R. Ferdiana, and I. Ardiyanto, “Deep learning pre-trained model as feature extraction in facial recognition for identification of electronic identity cards by considering age progressing,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1115, no. 1, p. 012009, Mar. 2021, doi: https://doi.org/10.1088/1757-899X/1115/1/012009
T. H. Tsai and P. T. Chi, “A single-stage face detection and face recognition deep neural network based on feature pyramid and triplet loss,” 2022. doi: https://doi.org/10.1049/ipr2.12479
M. Cardaioli, M. Conti, G. Orazi, P. P. Tricomi, and G. Tsudik, “BLUFADER: Blurred face detection & recognition for privacy-friendly continuous authentication,” Pervasive Mob. Comput., vol. 92, p. 101801, 2023, doi: https://doi.org/10.1016/j.pmcj.2023.101801.
S. Anwarul, T. Choudhury, and S. Dahiya, “A novel hybrid ensemble convolutional neural network for face recognition by optimizing hyperparameters,” Nonlinear Eng., vol. 12, no. 1, Jun. 2023, doi: https://doi.org/10.1515/nleng-2022-0290
P. A. Riyantoko, Sugiarto, and K. M. Hindrayani, “Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks,” J. Phys. Conf. Ser., vol. 1844, no. 1, p. 012004, Mar. 2021, doi: https://doi.org/10.1088/1742-6596/1844/1/012004
I. G. S. M. Diyasa, A. Fauzi, M. Idhom, and A. Setiawan, “Multi-face Recognition for the Detection of Prisoners in Jail using a Modified Cascade Classifier and CNN,” J. Phys. Conf. Ser., vol. 1844, no. 1, p. 012005, Mar. 2021, doi: https://doi.org/10.1088/1742-6596/1844/1/012005
E. Al Daoud and G. Samara, “Improving the Face Recognition Performance Using Gabor and VGGFace2 Features Concatenation,” in 2022 6th International Conference on Information Technology (InCIT), IEEE, Nov. 2022, pp. 187–190. doi: https://doi.org/10.1109/InCIT56086.2022.10067669
S. C. Hidayati, J. Tasyanita, C. Fatichah, and Y. Anistyasari, “Understanding Human Face Shape via Inception-ResNet Neural Network Architecture,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), IEEE, Nov. 2023, pp. 631–636. doi: https://doi.org/10.1109/ICAMIMIA60881.2023.10427897
O. Elharrouss, N. Almaadeed, and S. Al-Maadeed, “LFR face dataset:Left-Front-Right dataset for pose-invariant face recognition in the wild,” in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), IEEE, Feb. 2020, pp. 124–130. doi: https://doi.org/10.1109/ICIoT48696.2020.9089530
Shahjahan, N. Sheikh, A. Rehman, and M. Anjum, “Development of Face Recognition System Using Support Vector Machine Algorithm,” Eur. J. Appl. Sci. Eng. Technol., vol. 2, no. 3, pp. 214–227, May 2024, doi: https://doi.org/10.59324/ejaset.2024.2(3).20
H. R. Maier et al., “On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization,” Environ. Model. Softw., vol. 167, p. 105779, Sep. 2023, doi: https://doi.org/10.1016/j.envsoft.2023.105779
N. S. Vemulapalli, P. Paladugula, G. S. Prabhat, S. Abhishek, and A. T, “Face Detection with Landmark using YOLOv8,” in 2023 3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), IEEE, Dec. 2023, pp. 1–5. doi: https://doi.org/10.1109/ICEFEET59656.2023.10452204.
L. Huang, J. Qin, Y. Zhou, F. Zhu, L. Liu, and L. Shao, “Normalization Techniques in Training DNNs: Methodology, Analysis and Application,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 8, pp. 10173–10196, Aug. 2023, doi: https://doi.org/10.1109/TPAMI.2023.3250241
I. Y. Susrama, I. G. S. M. D., Putra, A. H., Ariefwan, M. R. M., Atnanda, P. A., Trianggraeni, F., & Purbasari, “Feature Extraction for Face Recognition Using Haar Cascade Classifier,” 2022. doi: https://doi.org/10.11594/nstp.2022.2432
K. Cao, “Novel coronavirus pneumonia CT image classification based on inception ResNet,” IET Conf. Proc., vol. 2024, no. 21, pp. 134–138, Jan. 2025, doi: https://doi.org/10.1049/icp.2024.4214
M. I. Hossain, Sama-E-Shan, and H. Kabir, “An efficient way to recognize faces using mean embeddings,” Proc. 2021 1st Int. Conf. Adv. Electr. Comput. Commun. Sustain. Technol. ICAECT 2021, no. October, 2021, doi: https://doi.org/10.1109/ICAECT49130.2021.9392401
N. Mardiana, R. D. Dana, Faisal, I. Farida, A. G. Azwar, and Nurwathi, “Similarity Measures Implementation on Face Authentication using Indonesian Citizen ID Card,” in 2023 17th International Conference on Telecommunication Systems, Services, and Applications (TSSA), IEEE, Oct. 2023, pp. 1–5. doi: https://doi.org/10.1109/TSSA59948.2023.10366880
H. Wu, Y. Cao, H. Wei, and Z. Tian, “Face Recognition Based on Haar like and Euclidean Distance,” J. Phys. Conf. Ser., vol. 1813, no. 1, 2021, doi: https://doi.org/10.1088/1742-6596/1813/1/012036
S. M. Sami, J. McCauley, S. Soleymani, N. Nasrabadi, and J. Dawson, “Benchmarking human face similarity using identical twins,” IET Biometrics, vol. 11, no. 5, pp. 459–484, 2022, doi: https://doi.org/10.1049/bme2.12090
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