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پانزدهمین کنفرانس بین المللی فناوری اطلاعات و دانش
A Multi-Task Framework Using Mamba for Identity, Age, and Gender Classification from Hand Images
Authors :
Amirabbas Rezasoltani
1
Alireza Hosseini
2
Ramin Toosi
3
MohammadAli Akhaee
4
1- دانشگاه تهران
2- دانشکده برق و کامپیوتر دانشگاه تهران
3- دانشکده برق و کامپیوتر دانشگاه تهران
4- دانشکده برق و کامپیوتر دانشگاه تهران
Keywords :
Person Identification،Biometric authentication،mamba،Multi-Task Framework،Gender Recognition،age classification،Hand Images
Abstract :
Biometric authentication is crucial for secure access, surpassing traditional password-based methods vulnerable to breaches. Non-intrusive techniques like hand-based biometrics offer unique advantages, using physiological traits to identify individuals and predict soft biometrics such as age and gender. However, most current systems are designed with a single-purpose focus. In this field, Convolutional Neural Networks (CNNs) are typically utilized but struggle to capture long-range dependencies in images. Transformers, while more effective at handling such relationships, come with high computational costs. To overcome these challenges, this study introduces a novel multi-task learning framework that predicts identity, age, and gender simultaneously. The framework integrates the efficient long-range dependency modeling of Mamba, utilizing Visual State-Space Models (VSS) to capture both local and global patterns with reduced computational complexity. Experiments on the 11k Hands dataset demonstrate superior or competitive performance compared to existing methods.
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