Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions

Facial creation: using compositing to conceal identity

This study focused on the creation of new faces by compositing features from donor face photographs together that provide a way to generate new face identities. However, does the act of compositing conceal the identity of the donor faces? Two applications of these created faces require donor face identities to remain concealed: Covert social media profiles provide a way for investigating authorities to survey online criminal activity and, as such, a false online identity, including face image, is required. Compositing features/face parts from various donor face photographs could be used to generate new face identities. Face donor photographs are also used for the ‘texturing’ of facial depictions to reconstruct an image of how a person might appear. This study investigated whether compositing unknown face features onto known familiar faces (celebrities and lecturers) was sufficient to conceal identity in a face recognition task paradigm. A first experiment manipulated individual features to establish a feature saliency hierarchy. The results of this informed the order of feature replacement for the second experiment, where features were replaced in a compound manner to establish how much of a face needs to be replaced to conceal identity. In line with previous literature, the eyes and hair were found to be highly salient, with the eyebrows and nose the least. As expected, the more features that are replaced, the less likely the face was to be recognised. A theoretical criterion point from old to new identity was found for the combined data (celebrity and lecturer) where replacing at least two features resulted in a significant decrease in recognition. Which feature was being replaced was found to have more of an effect during the middle part of feature replacement, around the criterion point, where the eyes were more important to be replaced than the mouth. Celebrities represented a higher level of familiarity and, therefore, may be a more stringent set of results for practical use, but with less power than the combined data to detect changes. This would suggest that at least three features (half the face) need to be replaced before recognition significantly decreases, especially if this includes the more salient features in the upper half of the face. However, once all six features were replaced, identity was not concealed 100% of the time, signifying that feature replacement alone was not sufficient to conceal identity. It is completely possible that residual configural and contrast information was facilitating recognition, and, therefore, it is likely that manipulations, such as these, are also needed in order to conceal identity