Enhanced Facial Recognition Using CNN and GoogleNet: A High-Performance Framework

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 9

Year of Publication : 2025

Authors : Sonam Chopade, J. M. Bhattad, Abhishek Madankar, Shital Telrandhe, Monali Gulhane, Prabhakar Khandait, Sadaf Hussain

10.14445/23488549/IJECE-V12I9P112

How to Cite?

Sonam Chopade, J. M. Bhattad, Abhishek Madankar, Shital Telrandhe, Monali Gulhane, Prabhakar Khandait, Sadaf Hussain, "Enhanced Facial Recognition Using CNN and GoogleNet: A High-Performance Framework," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 9, pp. 142-152, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I9P112

Abstract:

Facial recognition technology has become crucial in enhancing security, authentication, and interactions between people and devices. This paper introduces an Advanced Facial Recognition System that combines Convolutional Neural Networks (CNNs) with GoogleNet to improve classification performance. The suggested model was assessed using the CK+ (Cohn-Kanade) dataset, which is a commonly referenced benchmark for facial recognition and emotion evaluation. The machine learning model is evaluated using different parameters. The Training Accuracy, Validation Accuracy, and Testing Accuracy are around 99%, 100%, and 89.70%. The Precision, Recall, Specificity, and F1 Score are achieved in the range of 91.00%, 88.50%, 92.20%, and 89.70%. The results indicate a very good balance between the positive and negative false values. The model improved using SGDM and a Cross-Entropy Loss function, and was able to differentiate different facial identities very well, as proved with the help of a 0.94 ROC-AUC score. The training time required to execute the simulation is around 45 minutes. The batch sizes are kept at 32 and an LR of 0.01. The model attains convergence inside 6 epochs. The proposed model is compared with many machine learning models, such as ResNet-50, VGG-16, and MobileNet, on the basis of testing accuracy, precision, and specificity. The given model maintains lower computational costs and achieves faster convergence. The testing accuracy of ResNet-50 is 87.50% but it needs large computational resources. The VGG-16 model has an accuracy of 86.20% but it has an overfitting problem. The testing accuracy of Mobile Net is 85.10%, and it is used for mobile applications. Its accuracy is low compared to the CNN-GoogleNet. The findings confirm the proposed model, which is hybrid, combining CNN and GoogleNet, strikes a balance between precision and efficiency, making it well-suited for real-time facial recognition.

Keywords:

Convolutional Neural Networks (CNNs), CK+ dataset, OpenCV, MATLAB, Facial identification, Facial detection, Real-time image processing, Feature extraction, Deep learning, Facial expression recognition.

References:

[1] Zhong Xiaolei, “Improving Face Recognition Accuracy through Optimization of Haar and LBP Features in MATLAB,” Scientific Journal of Technology, vol. 6, no. 5, pp. 119-125, 2024.
[CrossRef] [Publisher Link]
[2] E.L. Arjun et al., “Advanced Face Authentication Using Deep Learning Models,” IEEE Pune Section International Conference, Pune, India, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Mallika Kohli et al., “Identification and Recognition of Facial Images,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 6, no. 5, pp. 69-76, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Medha Jha et al., “Face Recognition: Recent Advancements and Research Challenges,” 13th International Conference on Computing Communication and Networking Technologies, Kharagpur, India, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Shilpa Sharma, and Kumud Sachdeva, “Face Recognition using PCA and SVM with Surf Technique,” International Journal of Computer Applications, vol. 129, no. 4, pp. 41-46, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Priyanka Dhoke, and M.P. Parsai, “A MATLAB based Face Recognition using PCA with Back Propagation Neural Network,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 8, pp. 5291-5297, 2014.
[Google Scholar] [Publisher Link]
[7] Nawaf Hazim Barnouti, “Face Recognition using PCA-BPNN with DCT Implemented on Face94 and Grimace Databases,” International Journal of Computer Applications, vol. 142, no. 6, pp. 8-13, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Zhiming Qian, and Dan Xu, “Research Advances in Face Recognition,” Chinese Conference on Pattern Recognition, Nanjing, China, pp. 1-5, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[9] C.R. Vishwanatha et al., “Face Recognition and Identification Using Deep Learning,” Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, Bhilai, India, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Al Mahmud Zayeef, and Rana Jyoti Chakma, “Face Recognition-Based Automated Attendance System for Educational Institutions Utilizing Machine Learning,” Information and Communication Technology for Competitive Strategies, pp. 325-333, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Shahrin Azuan Nazeer, Nazaruddin Omar, and Marzuki Khalid, “Face Recognition System using Artificial Neural Networks Approach,” International Conference on Signal Processing, Communications and Networking, Chennai, India, pp. 420-425, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Jingu Heo, M. Savvides, and B.V.K. Vijayakumar, “Performance Evaluation of Face Recognition using Visual and Thermal Imagery with Advanced Correlation Filters,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops, San Diego, CA, USA, pp. 9-9, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Musab Coşkun et al., “Face Recognition with Convolutional Neural Network,” International Conference on Modern Electrical and Energy Systems, Kremenchuk, Ukraine, pp. 376-379, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Lee Hui Kueh, and Lee Jon-Tark, “Face Recognition Using Linear Discriminant Analysis (LDA) of Principal Component Analysis (PCA),” Proceedings of the 8th Symposium on Advanced Intelligent Systems, pp. 941-944, 2007.
[Google Scholar]
[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, pp. 1701-1708, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Florian Schroff, Dmitry Kalenichenko, and James Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823, 2015.
[Google Scholar] [Publisher Link]
[17] Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016.
[Google Scholar] [Publisher Link]
[18] Kaiming He et al., “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Priyansh Saxena, Akshat Maheshwari, and Saumil Maheshwari, “Predictive Modeling of Brain Tumor: A Deep Learning Approach,” arXiv preprint, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Karen Simonyan, and Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv Preprint, pp. 1-14, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Wisal Hashim Abdulsalam et al., “Automated Glaucoma Detection Techniques: A Literature Review,” Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19891-19897, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Andrew G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv Preprint, pp. 1-9, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Prasun Roy et al., “Effects of Degradations on Deep Neural Network Architectures,” arXiv Preprint, pp. 1-10, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Douglas O’Shaughnessy, “Recognition and Processing of Speech Signals Using Neural Networks,” Circuits, Systems, and Signal Processing, vol. 38, no. 8, pp. 3454-3481, 2018.
[CrossRef] [Google Scholar] [Publisher Link]