In general, the presented analysis suggests that, for each biometric modality, there are channels which can alone offer high PAD performance. However, the overall method followed in this work is by design not optimal for PAD purposes. In order to unveil the full power of multispectral data, one should use multiple input channels. Models employing multi-channel inputs can provide very high PAD accuracy. Additionally, an ideal model for multispectral data can further benefit from attention mechanisms (e.g., using transformers) or significant channel selection among all available input channels and early or late fusion methods. Even more, PAD methods could exploit the video nature of the captured data by observing liveness through human natural motion (e.g., eye blinking). The presented analysis herein does not aim to replace or be compared with optimal PAD methods. Instead, a single algorithmic approach was selected to facilitate a common analysis of different spectral bands on all studied biometric modalities and showcase the strengths and weaknesses of distinct regimes in the captured data. Certainly, shortcomings of PAD methods employing limited number of input channels, as the ones observed in the Cross-Dataset protocol analysis, could be alleviated by more sophisticated or robust classification methods (e.g., using pre-training, transfer learning or fine-tuning techniques), but the purpose of our analysis was not to design the best possible PAD classification model. Instead, we analyze the capabilities of the data captured through the proposed sensor suites in two ways. First, we capitalize on the limitations originating from certain wavelength regimes and their comparison to more effective spectral bands. Second, we stress the importance of the availability of a variety of spectral bands which offer complementary information that can be exploited for training robust classification models.

The presented framework and specific biometric sensor suite designs are the result of a multi-year effort during which multiple design decisions have been changed for improving performance. Our earlier systems utilized fewer wavelengths and their architecture was even more complex due to the use of lower-resolution cameras which required additional hardware components (e.g., fast-steering mirrors for finger) for a full observation of the biometric sample in certain regimes. On top of that, the capture was not synchronized and cameras were only controlled through software and had to be invoked sequentially to ensure capture under different illumination conditions. This resulted to very long capture durations and did not exploit the complementarity of spectra (e.g., an RGB camera cannot sense SWIR light). It soon became apparent that such an approach had various limitations. First, it led to the subject moving widely throughout the capture, making data alignment challenging. Second, the system was inherently low throughput due to the long capture durations (e.g., a full capture of a finger took around 2 minutes, versus 4.5 seconds in this work, while capturing less data). Employing such a system in a high-bandwidth environment was very limiting. As a result, we adopted a hardware-based design that allows simultaneous and synchronized capture by all available sensors while exploiting each sensor’s non-sensitivity to certain spectra, achieving the reduction of capture time to seconds. More importantly, the new design enabled the use of common hardware components on all biometric sensor suites, resulting to a unified framework that can be easily customized with different sensors and illumination sources to achieve specific application needs. Additionally, the REST API based software design allows system components to operate in a distributed fashion (on different machines, if needed), which proved very valuable in cases of computer system failures, in our experience.

Finally, we need to emphasize that the proposed framework is very general and can be adopted for different applications requiring high-bandwidth collection of multispectral data. Examples include, but are not limited to, multispectral material characterization or biomedical applications. Nevertheless, there is still room for improvement. Our design is an experimental prototype and could be certainly miniaturized by using custom-made sensors as well as a market-oriented compact design, if produced in mass scales. However, this would not interfere with the basic concepts of our framework that capitalize on the re-usability of common components on different, application-specific, multispectral sensor suites.