Facial Recognition and Object Detection using Machine learning

Abhishek Kumar; Kapil Kumar

doi:10.70454/JRICST.2025.20214

Authors

Abhishek Kumar Assistant Professor, College of Smart Computing, COER University, Roorkee. Author
Kapil Kumar Assistant Professor, College of Smart Computing, COER University, Roorkee. Author

DOI:

https://doi.org/10.70454/JRICST.2025.20214

Keywords:

Facial Recognition, Object Detection, Machine Learning, Deep Learning, Feature Extraction

Abstract

Facial recognition and object detection are critical computer vision problems used in security, surveillance, autonomous systems, and human-computer interaction. This study investigates the of machine learning techniques. Use of facial recognition and object detection in deep learning has been develop to high level using machine learning. In enhancing the performance and enhancing the generalization of the model, this studies implements a recognition system on VGG16 model with data augmentation. To feed into the AI model, 1800 images were extracted from 17 classes and pre-processed, normalized and augmented also through rotation and flipping. The VGG16 model architecture was used and then trained by using categorical cross entropy loss function and Adam optimizer. The model achieved a validation accuracy of 76.39% within five epochs, signifying the model’s potential on various facial variations. Some of the issues that were pointed out include shortcoming such as misidentification and low quality images. Further researches propose to expand the dataset used for the study, to employ more complex base architectures such as ResNet, and to augment data pre-processing to improve recognition accuracy.

References

[1] Teoh, K.H., Ismail, R.C., Naziri, S.Z.M., Hussin, R., Isa, M.N.M. and Basir, M.S.S.M., 2021, February. Face recognition and identification using deep learning approach. In Journal of Physics: Conference Series (Vol. 1755, No. 1, p. 012006). IOP Publishing.

[2] Prasad, P.S., Pathak, R., Gunjan, V.K. and Ramana Rao, H.V., 2019, August. Deep learning based representation for face recognition. In ICCCE 2019: Proceedings of the 2nd International Conference on Communications and Cyber Physical Engineering (pp. 419-424). Singapore: Springer Singapore.

[3] Hussain, S.A. and Al Balushi, A.S.A., 2020. A real time face emotion classification and recognition using deep learning model. In Journal of physics: Conference series (Vol. 1432, No. 1, p. 012087). IOP Publishing.

[4] Jin, B., Cruz, L. and Gonçalves, N., 2020. Deep facial diagnosis: deep transfer learning from face recognition to facial diagnosis. IEEe Access, 8, pp.123649-123661.

[5] Jiang, Z.P., Liu, Y.Y., Shao, Z.E. and Huang, K.W., 2021. An improved VGG16 model for pneumonia image classification. Applied Sciences, 11(23), p.11185.

[6] Pei, X., hong Zhao, Y., Chen, L., Guo, Q., Duan, Z., Pan, Y. and Hou, H., 2023. Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences. Materials & Design, 232, p.112086.

[7] Sharma, S., Guleria, K., Tiwari, S. and Kumar, S., 2022. A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans. Measurement: Sensors, 24, p.100506.

[8] Dogo, E.M., Afolabi, O.J. and Twala, B., 2022. On the relative impact of optimizers on convolutional neural networks with varying depth and width for image classification. Applied Sciences, 12(23), p.11976.

[9] Alruwaili, M. and Mohamed, M., 2025. An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification. Diagnostics, 15(5), p.551.

[10] B. Jin, L. Cruz and N. Gonçalves, "Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis," in IEEE Access, vol. 8, pp. 123649-123661, 2020, doi: 10.1109/ACCESS.2020.3005687.

[11] Hollywood Celebrity Facial Recognition Dataset,” Kaggle, Feb. 15, 2024. https://www.kaggle.com/datasets/bhaveshmittal/celebrity-face-recognition-dataset

[12]K. S. Sharmila, R.Thriveni and T. M. Priya, "Enhancing Crowd Image Analysis Through Facial Landmark Recognition," 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST), Bengaluru, India, 2024, pp. 1-5, doi: 10.1109/ICRISST59181.2024.10922068.

[13] S. Ren, M. Sun, B. Wang, M. Liu and S. Men, "High Precision Infant Facial Expression Recognition by Improved YOLOv8," in IEEE Access, vol. 13, pp. 39621-39630, 2025, doi: 10.1109/ACCESS.2025.3543950.

[14] C. Li, X. Zhang and C. Wu, "Facial Expression Recognition with DToF Sensing," ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5, doi: 10.1109/ICASSP49660.2025.10887978.

[15] J. N. A. Rodrigues et al., "Identification of Parkinson’s Disease Through Facial Image Classification: A Systematic Review," in IEEE Access, vol. 13, pp. 46720-46731, 2025, doi: 10.1109/ACCESS.2025.3549694.