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.