ROBUST REPRESENTATIONS FOR UNCONSTRAINED FACE RECOGNITION AND ITS APPLICATIONS

Face identification and verification are important problems in computer vision and have been actively researched for over two decades. There are several applications including mobile authentication, visual surveillance, social network analysis, and video content analysis. Many algorithms have shown to work well on images collected in controlled settings. However, the performance of these algorithms often degrades significantly on images that have large variations in pose, illumination and expression as well as due to aging, cosmetics, and occlusion. How to extract robust and discriminative feature representations from face images/videos is an important problem to achieve good performance in uncontrolled settings. In this dissertation, we present several approaches to extract robust feature representation from a set of images/video frames for face identification and verification problems. We first present a dictionary approach with dense facial landmark features. Each face video is segmented into K partitions first, and the multi-scale features are extracted from patches centered at detected facial landmarks. Then, compact and representative dictionaries are learned from dense features for each partition of a video and then concatenated together into a video dictionary representation for the video. Experiments show that the representation is effective for the unconstrained video-based face identification task. Secondly, we present a landmark-based Fisher vector approach for video-based face verification problems. This approach encodes over-complete local features into a high-dimensional feature representation followed by a learned joint Bayesian metric to project the feature vector into a low-dimensional space and to compute the similarity score. We then present an automated system for face verification which exploits features from deep convolutional neural networks (DCNN) trained using the CASIA-WebFace dataset. Our experimental results show that the DCNN model is able to characterize the face variations from the large-scale source face dataset and generalizes well to another smaller one. Finally, we also demonstrate that the model pre-trained for face identification and verification tasks encodes rich face information which benefit other face-related tasks with scarce annotated training data. We use apparent age estimation as an example and develop a cascade convolutional neural network framework which consists of age group classification and age regression, and a deep networks is fine-tuned using the target data

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