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Artificially intelligent perception is increasingly present in the lives ofevery one of us. Vehicles are no exception, (...) In the near future, patternrecognition will have an even stronger role in vehicles, as self-driving carswill require automated ways to understand what is happening around (and within)them and act accordingly. (...) This doctoral work focused on advancingin-vehicle sensing through the research of novel computer vision and patternrecognition methodologies for both biometrics and wellbeing monitoring. Themain focus has been on electrocardiogram (ECG) biometrics, a trait well-knownfor its potential for seamless driver monitoring. Major efforts were devoted toachieving improved performance in identification and identity verification inoff-the-person scenarios, well-known for increased noise and variability. Here,end-to-end deep learning ECG biometric solutions were proposed and importanttopics were addressed such as cross-database and long-term performance,waveform relevance through explainability, and interlead conversion. Facebiometrics, a natural complement to the ECG in seamless unconstrainedscenarios, was also studied in this work. The open challenges of masked facerecognition and interpretability in biometrics were tackled in an effort toevolve towards algorithms that are more transparent, trustworthy, and robust tosignificant occlusions. Within the topic of wellbeing monitoring, improvedsolutions to multimodal emotion recognition in groups of people andactivity/violence recognition in in-vehicle scenarios were proposed. At last,we also proposed a novel way to learn template security within end-to-endmodels, dismissing additional separate encryption processes, and aself-supervised learning approach tailored to sequential data, in order toensure data security and optimal performance. (...)

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring