Abstract
Multimodal systems are a workaround to enhance the robustness and effectiveness of biometric systems. A proper multimodal dataset is of the utmost importance to build such systems. The literature presents some multimodal datasets, although, to the best of our knowledge, there are no previous studies combining face, iris/eye, and vital signals such as the Electrocardiogram (ECG). Moreover, there is no methodology to guide the construction and evaluation of a chimeric dataset. Taking that fact into account, we propose to create a chimeric dataset from three modalities in this work: ECG, eye, and face. Based on the Doddington Zoo criteria, we also propose a generic and systematic protocol imposing constraints for the creation of homogeneous chimeric individuals, which allow us to perform a fair and reproducible benchmark. Moreover, we have proposed a multimodal approach for these modalities based on state-of-the-art deep representations built by convolutional neural networks. We conduct the experiments in the open-world verification mode and on two different scenarios (intra-session and inter-session), using three modalities from two datasets: CYBHi (ECG) and FRGC (eye and face). Our multimodal approach achieves impressive decidability of 7.20 ± 0.18, yielding an almost perfect verification system (i.e., Equal Error Rate (EER) of 0.20% ± 0.06) on the intra-session scenario with unknown data. On the inter-session scenario, we achieve a decidability of 7.78 ± 0.78 and an EER of 0.06% ± 0.06. In summary, these figures represent a gain of over 28% in decidability and a reduction over 11% of the EER on the intra-session scenario for unknown data compared to the best-known unimodal approach. Besides, we achieve an improvement greater than 22% in decidability and an EER reduction over 6% in the inter-session scenario.
Highlights
Robust and trustful mechanisms are required to protect our privacy, in particular when such information allows access to valuable goods or restricted places
The results presented here (Table 2) support the strength of this fusion, reducing the mean Equal Error Rate (EER) acquired with face recognition on Face Recognition Grand Challenge (FRGC) by more than 45% and 90% for the eye
We proposed a simple and reproducible protocol for chimeric datasets’ creation
Summary
Robust and trustful mechanisms are required to protect our privacy, in particular when such information allows access to valuable goods or restricted places In this direction, the digital methods for personal recognition are fundamental, and the most common practice is still using a Personal Identification Number (PIN) or merely a password. Password-based approaches usually are related to something familiar to the subject/individual or it is written in somewhere and encrypted, digitally stored This scenario is susceptible to several attacks in an attempt to steal important data. Due to these facts, more efficient ways to recognize an individual digitally have been investigated in the literature. Biometrics-based techniques are the most promising path
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