Chronological Age Estimation Under the Guidance of Age-Related Facial Attributes

Although the researches of facial attributes' analysis have been launched for decades, the estimation of chronological age attribute remains a big challenge. Previous researchers have found that some facial attributes (e.g,, gender and race attributes) have close connections with the age attribute and make age estimation under a specific condition decided by various combinations of those age-related attributes which should be more reasonable. In this paper, we propose a generic framework based on a convolutional neural network, which can consider different conditions for age estimation and jointly output age and agerelated facial attributes in the end. Compared with conventional methods, it is more efficient and universal. Besides, we view age estimation as a special multi-class ordinal classification problem and use a losses combination function to optimize the predicted probability distribution of individual age classes. These operations further improve the performance of age estimation. Finally, the proposed method achieves state-of-the-art results on both controlled and wild face datasets.