Individual Aging Pattern Modeling for Enhanced Facial Age Recognition

Age recognition from facial features is a pivotal area in computer vision and biometrics, with applications spanning precision marketing, healthcare, and public safety. However, the complexity and individual differences in the aging process pose significant challenges. This paper introduces an age recognition model based on Individual Aging Patterns (IAP), which leverages convolutional neural networks (CNN) for deep feature extraction, manifold learning for feature selection, and recurrent neural networks (RNN) with attention mechanisms to model the temporal sequence of facial aging. An improved age label distribution strategy is also employed to capture fuzzy age information. Experiments on FG-NET and MORPH datasets demonstrate that our IAP model significantly enhances age recognition accuracy, achieving Mean Absolute Errors (MAE) of 1.07 and 1.92, respectively.