Performance Analysis of Computer Vision Algorithms Based on Python in Artificial Intelligence Face Recognition

This paper delves into the performance of computer vision algorithms based on Python in artificial intelligence face recognition. Facial recognition, as a key technology in the field of computer vision, has been widely applied. Python has played an important role in the implementation and performance optimization of facial recognition algorithms due to its powerful functionality and ease of use. The study compared the performance of traditional feature extraction methods and deep learning models, such as convolutional neural networks, on publicly available datasets. Experiments have shown that deep learning algorithms have significant advantages in processing large-scale datasets and complex scenes. At the same time, the paper analyzed the impact of factors such as image quality, lighting, and posture on recognition performance, and proposed strategies such as data augmentation and model optimization to improve the model's generalization ability and recognition accuracy. Looking ahead to the future, Python based facial recognition algorithms will be more widely applied, while paying attention to privacy and security issues to promote their healthy development.