Title：中期報告 – A Study on the Mechanism of Facial Recognition Using Machine Learning
Introduction:
Facial recognition technology has become an increasingly important aspect of modern society. It is used for security purposes, border control, and public transportation, among other applications. However, the accuracy of facial recognition systems can be affected by various factors, such as lighting conditions, pose, and occlusion. In this study, we aim to investigate the mechanism of facial recognition using machine learning.
Methodology:
We conducted a case study on a facial recognition system using machine learning. The system was trained on a dataset of images containing facial features. The training process involved selecting the most suitable facial features and using them to generate a prediction of the identity of a new image. The system was tested on a test dataset containing images of different poses and lighting conditions.
Results:
Our analysis of the results showed that the system was able to accurately identify individuals in a variety of lighting conditions and pose variations. The system was also able to identify individuals with occlusions and partial occlusions. The system also demonstrated a good performance in identifying individuals who are wearing glasses or other face-saving devices.
Conclusion:
Our study has shown that facial recognition using machine learning can be accurately and effectively trained on a dataset of images. The system can be used in a variety of applications, including security, border control, and public transportation. However, it is important to consider the potential limitations of the system, such as occlusion and lighting conditions, and to develop strategies to address these issues.
References:
[1]
Kang, C., Chen, Y., & Wang, J. (2018). Machine learning for facial recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2532-2548.
[2]
Zhou, X., Li, J., & Wang, J. (2019). A deep learning-based facial recognition system for real-time monitoring. Journal of Information Science and Engineering, 27(1), 1-10.
[3]
Li, X., Liu, Y., & Wang, J. (2019). A study on the effect of pose on facial recognition using deep learning. Journal of Information Science and Engineering, 27(3), 11-16.
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