Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation

doi:10.1109/TIFS.2021.3098165

Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation

2021-01-01

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DOI：10.1109/TIFS.2021.3098165

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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. Moreover, we propose to use self-supervised learning to further explore the intrinsic semantics embedded in raw data and enrich the diversity of samples. As such, we could further improve the representation capabilities of kinship feature learning and mitigate the multiple variations from original visual images. 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.

Keywords:

Feature extraction

Task analysis

Visualization

Supervised learning

Correlation

Collaboration

Semantics

Kinship verification

information security

self-supervised learning

Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation

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