| Summary: | Nowadays, facial recognition has become very important in the field of computing and has been receiving a lot of attention over the years. Facial recognition can be used in many areas, but one area that has been growing a lot is security. Topics like access to military installations, identification of terrorist groups and people who force and abuse the law are some of the most discussed topics. Despite being a widely studied topic, there are still some limitations, especially when image acquisition is acquired from people in non-cooperative environments. The objective of this master’s thesis is the investigation of various methods of detection and facial recognition. It presents a study on the most important algorithms, and preprocessing techniques such as frontalization and facial alignment in order to be able to compare the accuracy levels of each of the algorithms. In order to obtain results, an image dataset was carried out at the University of Aveiro of various color spectrum. It was possible to observe that algorithms based on deep convolutional neural networks have a higher precision compared to several traditional methods. A first step was also taken towards developing a model of facial detection in thermal images, where there was an improvement of about 30% compared to the original model.
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