Deep User Identification Model with Multiple Biometrics
This work addresses the challenge of combining multiple biometric modalities for identification and gender classification, which is incremental as it builds on existing single-modality research.
The paper tackles the problem of biometric identification and gender classification by proposing a deep learning model that uses multimodal biometrics (ECG, fingerprint, and facial data) to perform both tasks simultaneously, achieving improved generalization through domain correlation without independent modality training.
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.