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.

Facial Expression Recognition Based on Deep Learning: A Review

“…Zhou et al [26] Addresses the limitations of intelligent facial recognition systems, focusing on the challenges of limited biometric features compared to methods like iris and fingerprint authentication. The proposed solution involves extracting facial feature information through facial expression key points and employing a spatiotemporal graph convolutional network (STGCN) fused with an attention mechanism for organizing and matching feature data.…”

“…Addressing limitations in facial recognition systems, the proposed STGCN with an attention mechanism significantly improves accuracy to 89%. Challenges related to side facial features recognition are acknowledged, emphasizing the need for future research to enhance performance in various orientations [26].…”