Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making

TL;DR

This survey comprehensively reviews a decade of research in visual kinship recognition, highlighting progress, challenges, datasets, methods, and future directions in this impactful area of computer vision.

Contribution

It provides a detailed overview of the evolution, datasets, and state-of-the-art methods in visual kinship recognition, establishing a foundation for future research.

Findings

Progress in visual kinship recognition over ten years

Analysis of datasets and data challenges

Evaluation of recent state-of-the-art methods

Abstract

Kinship recognition is a challenging problem with many practical applications. With much progress and milestones having been reached after ten years - we are now able to survey the research and create new milestones. We review the public resources and data challenges that enabled and inspired many to hone-in on the views of automatic kinship recognition in the visual domain. The different tasks are described in technical terms and syntax consistent across the problem domain and the practical value of each discussed and measured. State-of-the-art methods for visual kinship recognition problems, whether to discriminate between or generate from, are examined. As part of such, we review systems proposed as part of a recent data challenge held in conjunction with the 2020 IEEE Conference on Automatic Face and Gesture Recognition. We establish a stronghold for the state of progress for the different problems in a consistent manner. This survey will serve as the central resource for the work of the next decade to build upon. For the tenth anniversary, the demo code is provided for the various kin-based tasks. Detecting relatives with visual recognition and classifying the relationship is an area with high potential for impact in research and practice.IEEE Transactions on pattern analysis and machine intelligence