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 a...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.展开更多
Contrast-enhanced inner ear magnetic resonance imaging(MRI)provides clinicians with powerful structural and pathological information for the diagnosis of inner ear diseases.However,currently used gadolinium(Gd)chelate...Contrast-enhanced inner ear magnetic resonance imaging(MRI)provides clinicians with powerful structural and pathological information for the diagnosis of inner ear diseases.However,currently used gadolinium(Gd)chelate-mediated contrast-enhanced MRI conveys insufficient inner ear specificity,and Gd-based contrast agents have a short body retention time and cause severe ototoxicity.Herein,we present the rational design of a sensitive inner ear-specific nanoprobe(I-PUSPIO)for inner ear MRI that is composed of an ultrasmall iron oxide core,the IETP2 peptide,and polyethylene glycol.Such a welldefined nanostructure endows it with blood-labyrinth barrier crossing capacity,leading to a high accumulation rate in the inner ear and prolonged body retention.In vivo I-PUSPIO can enhance high-resolution MRI of cochlear tissue and shows no evidence of toxicity.This study demonstrates the potential of I-PUSPIO as a sensitive contrast agent for inner ear MRI in clinical settings.展开更多
文摘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.
基金supported by the National Natural Science Foundation of China(22475210,22105197)the Science and Technology Development Project of Jilin Province(20230101040JC)+1 种基金the Jilin Province Youth Scientific and Technological Talent Support Project(QT202403)the Sponsored by Beijing Nova Program(20230484315,20250484903).
文摘Contrast-enhanced inner ear magnetic resonance imaging(MRI)provides clinicians with powerful structural and pathological information for the diagnosis of inner ear diseases.However,currently used gadolinium(Gd)chelate-mediated contrast-enhanced MRI conveys insufficient inner ear specificity,and Gd-based contrast agents have a short body retention time and cause severe ototoxicity.Herein,we present the rational design of a sensitive inner ear-specific nanoprobe(I-PUSPIO)for inner ear MRI that is composed of an ultrasmall iron oxide core,the IETP2 peptide,and polyethylene glycol.Such a welldefined nanostructure endows it with blood-labyrinth barrier crossing capacity,leading to a high accumulation rate in the inner ear and prolonged body retention.In vivo I-PUSPIO can enhance high-resolution MRI of cochlear tissue and shows no evidence of toxicity.This study demonstrates the potential of I-PUSPIO as a sensitive contrast agent for inner ear MRI in clinical settings.
