The thesis addresses the challenge of recognizing individuals in an open set data environment. It presents an approach based on the use of methods for open set recognition using siamese neural networks and triplet loss. The main objective was to optimize existing recognition models using advanced techniques and algorithms and to conduct a comparative analysis between classical and optimized models. The results show that the optimized models do not exceed performance of classical models, indicating the proposed methods do not have the potential to be used in solving the problem of recognizing individuals in open set data environments, however they offer an implementation with better time performance. The acquired knowledge can serve as a foundation for further research and development in this field.
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