Development of a Face Prediction System for Missing Children in a Smart City Safety Network

Abstract

Cases of missing children not being found are rare, but they continue to occur. If the child is not found immediately, the parents may not be able to identify the child’s appearance because they have not seen their child for a long time. Therefore, our purpose is to predict children’s faces when they grow up and help parents search for missing children. DNA paternity testing is the most accurate way to detect whether two people have a blood relation. However, DNA paternity testing for every unidentified child would be costly. Therefore, we propose the development of the Face Prediction System for Missing Children in a Smart City Safety Network. It can predict the faces of missing children at their current age, and parents can quickly confirm the possibility of blood relations with any unidentified child. The advantage is that it can eliminate incorrect matches and narrow down the search at a low cost. Our system combines StyleGAN2 and FaceNet methods to achieve prediction. StyleGAN2 is used to style mix two face images. FaceNet is used to compare the similarity of two face images. Experiments show that the similarity between predicted and expected results is more than 75%. This means that the system can well predict children’s faces when they grow up. Our system has more natural and higher similarity comparison results than Conditional Adversarial Autoencoder (CAAE), High Resolution Face Age Editing (HRFAE) and Identity-Preserved Conditional Generative Adversarial Networks (IPCGAN).