A Social Assistance System for Augmented Reality Technology to Redound Face Blindness with 3D Face Recognition

The objective of this study is to develop an Augmented Reality (AR) visual aid system to help patients with prosopagnosia recognize faces in social situations and everyday life. The primary contribution of this study is the use of 3D face models as the basis of data augmentation for facial recognition, which has practical applications for various social situations that patients with prosopagnosia find themselves in. The study comprises the following components: First, the affordances of Active Stereoscopy and stereo cameras were combined. Second, deep learning was employed to reconstruct a detailed 3D face model in real-time based on data from the 3D point cloud and the 2D image. Data were also retrieved from seven angles of the subject’s face to improve the accuracy of face recognition from the subject’s profile and in a range of dynamic interactions. Second, the data derived from the first step were entered into a convolutional neural network (CNN), which then generated a 128-dimensional characteristic vector. Next, the system deployed Structured Query Language (SQL) to compute and compare Euclidean distances to determine the smallest Euclidean distance and match it to the name that corresponded to the face; tagged face data were projected by the camera onto the AR lenses. The findings of this study show that our AR system has a robustness of more than 99% in terms of face recognition. This method offers a higher practical value than traditional 2D face recognition methods when it comes to large-pose 3D face recognition in day-to-day life.