Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation

TL;DR

This paper introduces a deep collaborative multi-modal learning approach that integrates multiple facial properties like age and race to improve unsupervised kinship verification, outperforming existing methods.

Contribution

The paper proposes a novel adaptive feature fusion mechanism with an attention strategy for integrating multi-modal facial properties in kinship verification.

Findings

Outperforms state-of-the-art kinship verification methods on four datasets.

Effectively leverages multi-modal facial properties for improved accuracy.

Demonstrates robustness across diverse visual perspectives.

Abstract

Kinship verification is a long-standing research challenge in computer vision. The visual differences presented to the face have a significant effect on the recognition capabilities of the kinship systems. We argue that aggregating multiple visual knowledge can better describe the characteristics of the subject for precise kinship identification. Typically, the age-invariant features can represent more natural facial details. Such age-related transformations are essential for face recognition due to the biological effects of aging. However, the existing methods mainly focus on employing the single-view image features for kinship identification, while more meaningful visual properties such as race and age are directly ignored in the feature learning step. To this end, we propose a novel deep collaborative multi-modal learning (DCML) to integrate the underlying information presented in facial properties in an adaptive manner to strengthen the facial details for effective unsupervised kinship verification. Specifically, we construct a well-designed adaptive feature fusion mechanism, which can jointly leverage the complementary properties from different visual perspectives to produce composite features and draw greater attention to the most informative components of spatial feature maps. Particularly, an adaptive weighting strategy is developed based on a novel attention mechanism, which can enhance the dependencies between different properties by decreasing the information redundancy in channels in a self-adaptive manner. To validate the effectiveness of the proposed method, extensive experimental evaluations conducted on four widely-used datasets show that our DCML method is always superior to some state-of-the-art kinship verification methods.