Light Field Based Biometric Recognition and Presentation Attack Detection

In a world where security issues have been gaining explosive importance, face and ear recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. While the recognition performance has been steadily improving, there are still challenging recognition scenarios and conditions, notably when facing large variations in the biometric data characteristics. Additionally, the widespread use of face and ear recognition solutions raises new security concerns, making the robustness against presentation attacks a very active field of research. Lenslet light field cameras have recently come into prominence as they are able to also capture the intensity of the light rays coming from multiple directions, thus offering a richer representation of the visual scene, notably spatio-angular information. To take benefit of this richer representation, light field cameras have recently been successfully applied, not only to biometric recognition, but also to biometric Presentation Attack Detection (PAD). This Thesis focuses on exploiting the advances in light field imaging technology towards developing more advanced biometric recognition and PAD systems with improved performance. In this context, new taxonomies have been developed for face and ear recognition and PAD, to facilitate the organization and categorization of face and ear recognition and PAD solutions. Following the proposed taxonomies, a comprehensive review on recent, representative and relevant face and ear recognition solutions has been made. In the context of this Thesis, two light field face and ear databases have been created, towards allowing more powerful benchmarking for testing and validating face and ear recognition solutions while exploiting the full light field data. Additionally, two light field face and ear artefact databases have been created consisting of bona fide images and artefact images using different types of presentation attack instruments, such as printed papers and digital displays. Concerning recognition and PAD solutions, two hand-crafted light field based face and ear recognition descriptors and five deep learning light field based face recognition descriptors have been developed, evolving through progressive levels of functionality and performance. Concerning PAD, this Thesis has developed two solutions for light field based face and ear PAD, exploiting the variations associated to different directions of light captured in the light field images. A comprehensive evaluation of the proposed and benchmarking face and ear recognition and PAD solutions has been performed. The obtained results have shown the added value of light field information for face and ear recognition and PAD purposes as the proposed solutions have achieved superior recognition and PAD performance when compared to the state-of-the-art benchmarking solutions.