Age Estimation Model Using Multi-Class Architecture and Adaptive Regression Models

Age estimation is important in computer vision and surveillance systems. In this paper, we apply a two-stage and multi-class learning-based architecture to perform age estimation. First, the concepts of combining loss functions and domain adaptation are introduced to optimize the extracted features. Then, through a dual classification method, the input image is roughly classified into 10 classes. Then, a variety of regression models are integrated to obtain the final prediction results. Different classes apply different regression models. Simulations show that the proposed algorithm outperforms many well-known methods in age estimation. ? 2021 IEEE.