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Deep user identification model with multiple biometric data.

Hyoung-Kyu Song¹, Ebrahim AlAlkeem^2,3, Jaewoong Yun⁴

¹Korea Advanced Institute of Science and Technology, Daejeon, South Korea.

BMC Bioinformatics

|July 18, 2020

Summary

This summary is machine-generated.

This study introduces a novel multimodal and multitask deep learning model for human identification. The model enhances robustness and accuracy by combining multiple data inputs, outperforming single-modality approaches, especially against noisy data and spoofing attacks.

Keywords:

Multimodal learning Multitask learning Person identification

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Area of Science:

Computer Science
Biometrics
Artificial Intelligence

Background:

Human recognition relies on multiple sensory inputs like voice, face, and gestures.
Deep learning (DL) models with multi-modality offer advantages such as noise reduction.
ResNet-50 was utilized for feature extraction from 2D datasets.

Purpose of the Study:

To develop a novel multimodal and multitask model for simultaneous human identification and gender classification.
To investigate the benefits of combining multiple data modalities in a single deep learning framework.
To enhance the robustness and performance of human identification systems.

Main Methods:

A novel multimodal and multitask deep learning model was designed.
Features extracted from different modalities (ECG, face, fingerprint) were concatenated.
The model architecture allows for flexible adjustment of the number of input modalities.
A dataset of 58 virtual subjects was generated using public data.

Main Results:

The proposed model successfully performs human identification and gender classification in a single step.
Multimodal input demonstrated superior robustness and performance compared to single-modality inputs, particularly under noisy conditions.
The model exhibited robustness against spoofing attacks, crucial for bio-authentication.

Conclusions:

An end-to-end approach for multimodal and multitask learning was presented.
The model offers a significant improvement for human identification tasks, outperforming previous methods.
The findings suggest a promising new direction for developing secure and reliable bio-authentication devices.