Design of an Intelligent Laboratory Facial Recognition System Based on Expression Keypoint Extraction

Abstract

The number of clever facial recognition systems has been growing as artificial intelligence and robotics have advanced. However, due to the limited collection of biometric features by intelligent facial recognition systems compared to authentication methods such as iris and fingerprint, there are errors in the recognition process, low recognition accuracy, and low operational efficiency. To improve laboratory security and the efficiency of intelligent facial recognition systems, a study was conducted to extract facial feature information through facial expression key points, and a spatiotemporal graph convolutional network fused with attention mechanism was used to organize and match feature data. Finally, an improved facial recognition system was designed. A simulation experiment was conducted, and the results showed that the intelligent laboratory facial recognition system constructed by the research had a facial recognition accuracy of 89% in different datasets. Compared with the Line Hausdorff distance facial recognition method, the facial feature data was more concentrated, proving that the facial recognition system designed by the research has high recognition accuracy, wide application range, and strong application value, It helps to improve the optimization of facial recognition systems in the laboratory and improve the operational efficiency of the laboratory.

Keywords

• Facial recognition
• face keypoints
• GCNNs
• self-attention mechanism
• feature fusion