Recurrent age estimation

Age estimation is a challenging research topic in recent years. Existing approaches usually use only appearance features for age estimation. Personalized aging patterns, i.e., sequences of personal features, which have been shown as an important factor for improving age estimation accuracy, however, are not considered in their researches. We propose a novel model named recurrent age estimation (RAE), to make full use of appearance features as well as personalized aging patterns. RAE uses the CNN-LSTM architecture. Convolutional neural networks (CNNs) are trained to extract discriminative appearance features from face images, and long short-term memory networks (LSTMs) are employed to learn personalized aging patterns from sequences of personal features. Furthermore, we integrate the label distribution learning (LDL) scheme into LSTMs to exploit ambiguity from the real age and adjacent ages. The superiority of the RAE compared with existing approaches is shown by experimental results. (C) 2019 Elsevier B.V. All rights reserved.