Automatic face recognition has long been studied because it has a wide potential for application. Several systems have been developed to identify faces from small face populations via detailed face feature analysis, or by using neural nets, or through model based approaches. This study has aimed to provide satisfactory recognition within large populations of human faces and has concentrated on improving feature definition and extraction to establish an extended feature set to lead to a fully structured recognition system based on a single frontal view. An overall review on the development and the techniques of automatic face recognition is included, and performances of earlier systems are discussed. A novel profile description has been achieved from a frontal view of a face and is represented by a Walsh power spectrum which was selected from seven different descriptions due to its ability to distinguish the differences between profiles of different faces. A further feature has concerned the face contour which is extracted by iterative curve fitting and described by normalized Fourier descriptors. To accompany an extended set of geometric measurements, the eye region feature is described statistically by eye-centred moments. Hair texture has also been studied for the purpose of segmenting it from other parts of the face and to investigate the possibility of using it as a set of feature. These new features combine to form an extended feature vector to describe a face. The algorithms for feature extraction have been implemented on face images from different subjects and multiple views from the same person but without the face normal to the camera or without constant illumination. Features have been assessed in consequence on each feature set separately and on the composite feature vector. The results have continued to emphasize that though each description can be used to recognise a face there is a clear need for an extended feature set to cope with the requirements of recognizing faces within large populations.

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This thesis focuses on the development of an open source software capable of automatic face detection in an image captured by a thermal camera, followed by a temperature measuring. This software is supposed to aid in the COVID-19 pandemics. The developed software is independent of used thermal camera. In this thesis, I am using TIM400 thermal camera. The implementation of the face detection was achieved by an OpenCV module. The methods tested were Template Matching, Eigen Faces, and Cascade Classifier. The last-mentioned had the best results, hence was used in the final version of the software. Cascade Classifier is looking for the eyes and their surrounding area in the image, allowing the software to subsequently measure the temperature on the surface of one's forehead. One can therefore be wearing a face mask or a respirator safely. The temperature measuring works in real time and the software is able to capture several people at once. It then keeps a record of the temperature of each measured individual as well as the time of the measurement. The software as a whole is a part of an installation file compatible with the Windows operating system. The functionality of this software was tested – the video recordings are included in this thesis.

Morphing, ein Spezialeffekt zur Generierung eines Übergangs von einem Bild zum anderen, hat seinen Ursprung in der Filmindustrie, kann aber auch für kriminelle Zwecke missbraucht werden. Ein Zwischenbild eines Morphs, der das Gesicht einer Person in das einer anderen Person überführt, ähnelt beiden Gesichtern. Wenn ein solches Bild für einen Ausweis oder Reisepass verwendet wird, können beide behaupten, dessen Eigentümer zu sein. So könnten sich beide beispielsweise ein personengebundenes Verkehrsticket teilen oder es könnten illegal und unbemerkt Ländergrenzen überquert werden. Diese Dissertation stellt neue, auf neuronalen Netzen basierende Methoden zur Erkennung von Gesichtsmorphs und zur Lokalisierung von Fälschungsspuren vor. In Experimenten mit teilweise gemorphten Bildern wird gezeigt, dass die vorgestellten Detektoren in Kombination mit der vorgestellten Erklärbarkeitsmethode wesentlich genauer Fälschungsspuren lokalisieren können als andere gängige Methoden. Zum Trainieren der in der Arbeit entwickelten Detektoren wird eine große Menge an repräsentativen Daten benötigt. Daher legt diese Dissertation einen Schwerpunkt auf die automatische Erstellung von Gesichtsmorphs. Dazu stellt sie zwei Methoden vor, die Artefakte, die durch den Registrierungs- und Überblendungsschritt beim Morphing entstehen, deutlich reduzieren oder sogar vermeiden. Beide Verbesserungsmethoden ahmen die Möglichkeiten nach, die ein Angreifer durch manuelle Anpassungen hat. Die vorgestellten Detektoren wurden auf internen und externen Datensätzen evaluiert. Zusätzlich wurde ein Detektor bei einem international anerkannten Benchmark eingereicht. Dabei übertraf dieser andere Einreichungen in mehreren Kategorien deutlich. Zusammenfassend stellt diese Arbeit einen robusten und transparenten Detektor für gemorphte Gesichtsbilder vor, der Fälschungsspuren akkurat lokalisiert, mit dem Ziel einer nachvollziehbareren Klassifikation, sowie neue Methoden zur Erstellung von hochwertigen Gesichtsmorphs. / Morphing, as a smooth transformation of one image into another, originated in the cinematic industry. Beyond its entertainment applications, it can also be used for malicious purposes. An intermediate step of the morph from one person's face to that of a different one results in a synthetic face image that resembles both persons. If such an image is used for an ID card or passport, two individuals could claim ownership and share the associated privileges. Consequences can range from sharing a personal ticket for public transportation to entering a country unnoticed and without permission. This dissertation introduces novel methods for detecting morphed face images using Deep Neural Networks and proposes approaches to precisely identify traces of forgery. Experiments with partially morphed face images show that the proposed detection approaches in combination with this explainability method outperform other methods. A prerequisite for developing machine learning-based detectors is to have a substantial amount of representative data. Therefore, this thesis also emphasizes the automatic generation of morphed images and proposes two methods that mitigate artifacts caused by the alignment and blending step of the face morphing process. These improvement methods mimic the capabilities an attacker has through manual adjustments. The proposed detectors are evaluated on internal and on external datasets. Additionally, a proposed detector was submitted to an internationally renowned challenge. In this external benchmark, the submitted detector significantly outperforms other state-of-the-art submissions across multiple categories. As a summary, this thesis introduces a robust and transparent face morphing detector that is capable of highlighting detected traces of forgery to support humans in understanding the detector's decision, as well as advanced methods to improve the automatic generation of morphed face images.