Face image retrieval underpins numerous applications in many computer vision domains, however facial appearance variations including age, gender and race make this task challenging. Prior art methods rely on geometric properties and relationship between local features. However, their performance is still short of what is needed, mainly because (1) they ignore the demographic information, and (2) lack age-invariant re-ranking while retrieving face images. In this paper, we aim to build a two-stage face retrieval approach. First, we search for candidate face images using demographic-assisted clustering resulting in a short search space. Second, we develop a generative model to compensate aging variations between query and candidate face images resulting into an independent aging synthesized face images reference set. We then use this reference set to re-rank candidate face images resulting into the final retrieval of face images. We show that the proposed face retrieval approach outperforms the state-of-the-art methods in terms of both the precision and scalability on publicly available longitudinal datasets including CACD and MORPH II.

面部图像检索是许多计算机视觉领域中众多应用的基础，但是面部外观的变化（包括年龄，性别和种族）使这项任务具有挑战性。现有技术方法依赖于几何特性和局部特征之间的关系。但是，它们的性能仍然不足以满足其需求，这主要是因为（1）他们忽略了人口统计信息，并且（2）在检索人脸图像时缺乏按年龄不变的重新排名。在本文中，我们旨在建立一个两阶段的面部检索方法。首先，我们使用人口统计聚类搜索候选人脸图像，从而导致搜索空间短。其次，我们开发了一个生成模型来补偿查询和候选人脸图像之间的时效变化，从而生成独立的人脸合成面孔图像参考集。然后，我们使用此参考集重新排序候选人脸图像，从而最终检索出人脸图像。我们表明，就包括CACD和MORPH II在内的公开纵向数据集的准确性和可扩展性而言，拟议的人脸检索方法的性能优于最新方法。