Data driven analysis of faces from images

This thesis proposes three new data-driven approaches to detect, analyze, or modify faces in images. All presented contributions are inspired by the use of prior knowledge and they derive information about facial appearances from pre-collected databases of images or 3D face models. First, we contribute an approach that extends a widely-used monocular face detector by an additional classifier that evaluates disparity maps of a passive stereo camera. The algorithm runs in real-time and significantly reduces the number of false positives compared to the monocular approach. Next, with a many-core implementation of the detector, we train view-dependent face detectors based on tailored views which guarantee that the statistical variability is fully covered. These detectors are superior to the state of the art on a challenging dataset and can be trained in an automated procedure. Finally, we contribute a model describing the relation of facial appearance and makeup. The approach extracts makeup from before/after images of faces and allows to modify faces in images. Applications such as machine-suggested makeup can improve perceived attractiveness as shown in a perceptual study. In summary, the presented methods help improve the outcome of face detection algorithms, ease and automate their training procedures and the modification of faces in images. Moreover, their data-driven nature enables new and powerful applications arising from the use of prior knowledge and statistical analyses.In der vorliegenden Arbeit werden drei neue, datengetriebene Methoden vorgestellt, die Gesichter in Abbildungen detektieren, analysieren oder modifizieren. Alle Algorithmen extrahieren dabei Vorwissen über Gesichter und deren Erscheinungsformen aus zuvor erstellten Gesichts- Datenbanken, in 2-D oder 3-D. Zunächst wird ein weit verbreiteter monokularer Gesichtsdetektions- Algorithmus um einen zweiten Klassifikator erweitert. In Echtzeit wertet dieser stereoskopische Tiefenkarten aus und führt so zu nachweislich weniger falsch detektierten Gesichtern. Anschließend wird der Basis-Algorithmus durch Parallelisierung verbessert und mit synthetisch generierten Bilddaten trainiert. Diese garantieren die volle Nutzung des verfügbaren Varianzspektrums. So erzeugte Detektoren übertreffen bisher präsentierte Detektoren auf einem schwierigen Datensatz und können automatisch erzeugt werden. Abschließend wird ein Datenmodell für Gesichts-Make-up vorgestellt. Dieses extrahiert Make-up aus Vorher/Nachher-Fotos und kann Gesichter in Abbildungen modifizieren. In einer Studie wird gezeigt, dass vom Computer empfohlenes Make-up die wahrgenommene Attraktivität von Gesichtern steigert. Zusammengefasst verbessern die gezeigten Methoden die Ergebnisse von Gesichtsdetektoren, erleichtern und automatisieren ihre Trainingsprozedur sowie die automatische Veränderung von Gesichtern in Abbildungen. Durch Extraktion von Vorwissen und statistische Datenanalyse entstehen zudem neuartige Anwendungsfelder

Improving face perception and quality of life in age-related macular degeneration

The ability to see faces is essential for successful social interactions and good quality of life. Age-related macular degeneration (AMD) is a progressive eye condition that damages central vision required to see faces clearly. This thesis aims to investigate potential means to improve quality of life in AMD, via a two-pronged approach. The first prong examines the importance of face recognition difficulties, using a qualitative study of the effects of poor face perception in AMD on social interactions and quality of life. Previous studies of the impact of AMD on quality of life have focussed on domains including reading, driving, and self-care. Paper 1 of the thesis presents the first in-depth study of the quality-of-life impacts arising specifically from poor face perception. Results showed that, across all levels of vision loss (still driving through legally blind), AMD patients experience everyday problems with recognising who people are (face identity) and their emotions (facial expressions). These result in difficulties in social interactions, fear of offending others (e.g., appearing to ignore them deliberately), misinterpreting how others are feeling, and missing out in social situations. Patients also reported others did not understand their vision loss, and worried about appearing a fraud. These outcomes often contributed to social withdrawal and reduced confidence and quality of life. Paper 1 uses the study findings to develop new community resources (Faces and Social Life in AMD information sheet, conversation-starter, brochure for low-vision clinics), intended to improve patient and community understanding of how AMD affects face perception, and to provide practical tips for improving social interactions. The second prong focusses on improving face perception in AMD patients via image enhancement. The broad idea here is that, potentially, face images can be displayed to patients on screens or smart glasses after being digitally altered in ways that make them easier for patients to see and interpret. The specific image enhancement tested here is caricaturing, which involved exaggerating the shape information in the face image away from the average face (for face identity) or a neutral expression (for face expression). Paper 2 demonstrates that caricaturing can improve perception of identity in AMD; this benefit was observed for all eyes tested with mild vision loss, and half of eyes tested with moderate-to-severe vision loss. Paper 3 demonstrated that caricaturing can improve perception of facial expression in AMD, particularly for low-intensity expressions that are poorly recognised in their natural form, again across a wide range of vision loss. Overall, this thesis demonstrates that poor face perception in AMD is an important contributor to patients’ reduced quality of life. With the aim of enhancing quality of life, I have developed resources to improve community understanding, plus demonstrated that caricaturing provides a useful image enhancement method in AMD. Future research should focus on: further evaluation of the helpfulness of the community resources (to patients, carers and orthoptists); testing whether combining image enhancement methods (e.g., caricaturing plus contrast manipulations) can further improve face perception; and engineering advances needed to implement accurate caricaturing for patients in real-time

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well