Deep User Identification Model with Multiple Biometrics

TL;DR

This paper introduces a deep learning model that combines multiple biometric modalities like ECG, fingerprint, and face to improve identification and gender classification accuracy.

Contribution

It proposes a novel multimodal biometric model that handles various input domains simultaneously, enhancing generalization without separate training for each modality.

Findings

Multi-modality improves identification accuracy.

Single model effectively handles multiple biometric inputs.

Enhanced generalization performance observed.

Abstract

Identification using biometrics is an important yet challenging task. Abundant research has been conducted on identifying personal identity or gender using given signals. Various types of biometrics such as electrocardiogram (ECG), electroencephalogram (EEG), face, fingerprint, and voice have been used for these tasks. Most research has only focused on single modality or a single task, while the combination of input modality or tasks is yet to be investigated. In this paper, we propose deep identification and gender classification using multimodal biometrics. Our model uses ECG, fingerprint, and facial data. It then performs two tasks: gender identification and classification. By engaging multi-modality, a single model can handle various input domains without training each modality independently, and the correlation between domains can increase its generalization performance on the tasks.