Short search space and synthesized-reference re-ranking for face image retrieval

Abstract

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.