Enhanced Kinship Verification through Ear Images: A Comparative Study of CNNs, Attention Mechanisms, and MLP Mixer Models

Kinship verification is a key biometric recognition task that determines biological relationships based on physical features. Traditional methods predominantly use facial recognition, leveraging established techniques and extensive datasets. However, recent research has highlighted ear recognition as a promising alternative, offering advantages in robustness against variations in facial expressions, aging, and occlusions. Despite its potential, a significant challenge in ear-based kinship verification is the lack of large-scale datasets necessary for training deep learning models effectively. To address this challenge, we introduce the EarKinshipVN dataset, a novel and extensive collection of ear images designed specifically for kinship verification. This dataset consists of 4876 high-resolution color images from 157 multiracial families across different regions, forming 73,220 kinship pairs. EarKinshipVN, a diverse and large-scale dataset, advances kinship verification research using ear features. Furthermore, we propose the Mixer Attention Inception (MAI) model, an improved architecture that enhances feature extraction and classification accuracy. The MAI model fuses Inceptionv4 and MLP Mixer, integrating four attention mechanisms to enhance spatial and channel-wise feature representation. Experimental results demonstrate that MAI significantly outperforms traditional backbone architectures. It achieves an accuracy of 98.71%, surpassing Vision Transformer models while reducing computational complexity by up to 95% in parameter usage. These findings suggest that ear-based kinship verification, combined with an optimized deep learning model and a comprehensive dataset, holds significant promise for biometric applications.