Facial recognition access control system based on image enhancement and improved PCA algorithm

Applying artificial intelligence algorithms to non-contact biological features in facial access control systems can solve problems such as key dependence and untraceable visitors. A model based on image enhancement and improved Principal Component Analysis (PCA) algorithm is proposed to address the limitations of feature recognition and response delay in current research. The model utilizes feature extraction, dimensionality reduction, and multi-level feature fusion for recognition. Experiments show that the accuracy of the model is about 95% on the network face dataset of the Institute of Automation, Chinese Academy of Sciences, the average detection rate on the World Wide face database is 97.6%, and the average recognition accuracy on the self-made dataset is 98.9%, 96.8%, 95.4%. All results are better than the comparison method. In summary, the model exhibits high efficiency, accuracy, and strong generalization in multiple scenarios, effectively improving the current lack of accuracy and speed in recognition. We hope to further study its performance in reliability and robustness in the future.