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ACLMHA and FML: A brain-inspired kinship verification framework.

Chen Li¹, Menghan Bai¹, Lipei Zhang¹

¹School of Information, North China University of Technology, Beijing, China.

Frontiers in Neuroscience

|December 29, 2022

Summary

This study introduces a novel deep learning architecture for face image-based kinship verification, improving accuracy in identifying family relationships. The proposed method enhances feature learning and similarity measurement for more reliable kinship detection.

Keywords:

brain-inspired deep learning facial kinship verification multi-head attention relation comparison network

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Area of Science:

Computer Science
Artificial Intelligence
Biometrics

Background:

Kinship verification from face images is a challenging extension of face recognition.
Existing methods struggle with accurate intra-class and inter-class distance measurements.
Applications include missing persons searches and family data analysis.

Purpose of the Study:

To propose a novel deep learning architecture for improved face image-based kinship verification.
To enhance the model's ability to capture diverse local facial features.
To refine the measurement of feature similarity among relatives.

Main Methods:

An attention center learning guided multi-head attention mechanism to focus on distinct facial regions.
A family-level multi-center loss function for better intra/inter-class distance measurement.
A relation comparison module to assess feature similarity at a deeper level.

Main Results:

The proposed architecture achieved encouraging results on the Family in the Wild (FIW) dataset.
The method demonstrated superior performance compared to state-of-the-art approaches.
Experimental validation confirmed the effectiveness of the proposed components.

Conclusions:

The novel kinship verification architecture significantly advances the field.
The attention mechanism and multi-center loss effectively improve feature learning and classification.
The approach offers a more robust solution for real-world kinship verification applications.