Application and Performance Optimization of Facial Recognition Technology in OpenCV

This paper explores the application and performance optimization of facial recognition technology under the OpenCV framework, with a focus on introducing classic algorithms such as Eigenfaces and LBPH, as well as the latest progress of deep learning methods in the field of facial recognition. By reviewing the development history of facial recognition technology, from geometric features to statistical methods, and then to deep learning-driven recognition systems, this paper emphasizes the importance of technological innovation in improving recognition performance. As a core tool in the field of computer vision, OpenCV not only provides the implementation of various facial recognition algorithms but also supports the integration of deep learning models, greatly promoting the popularization and optimization of facial recognition technology. The paper provides a detailed analysis of the principles of Eigenfaces and LBPH algorithms and their applications in OpenCV while comparing the differences in accuracy and processing speed between traditional methods and deep learning methods. The experimental results show that although deep learning methods have significant advantages in accuracy, they have higher computational costs, while traditional algorithms such as Eigenfaces and LBPH have better computational efficiency, although their recognition accuracy is limited in complex environments. Finally, this paper demonstrates the trend of algorithm performance improving with increasing data volume by comparing the accuracy changes of different algorithms when increasing the number of training samples, providing insights for future research directions and application prospects of facial recognition technology.