Age estimation is a challenging task that has numerous applications. In this
paper, we propose a new direction for age classification that utilizes a
video-based model to address challenges such as occlusions, low-resolution, and
lighting conditions. To address these challenges, we propose AgeFormer which
utilizes spatio-temporal information on the dynamics of the entire body
dominating face-based methods for age classification. Our novel two-stream
architecture uses TimeSformer and EfficientNet as backbones, to effectively
capture both facial and body dynamics information for efficient and accurate
age estimation in videos. Furthermore, to fill the gap in predicting age in
real-world situations from videos, we construct a video dataset called Pexels
Age (P-Age) for age classification. The proposed method achieves superior
results compared to existing face-based age estimation methods and is evaluated
in situations where the face is highly occluded, blurred, or masked. The method
is also cross-tested on a variety of challenging video datasets such as
Charades, Smarthome, and Thumos-14.

利用视频模型和时空信息，提出 AgeFormer 方法用于年龄分类，有效地应对遮挡、低分辨率和光照等挑战，并通过构建 P-Age 数据集在面部高度遮挡、模糊或掩蔽的情况下，取得比现有基于面部的年龄估计方法更优的结果，同时在 Charades、Smarthome 和 Thumos-14 等多个具有挑战性的视频数据集上进行了跨测试。