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Mixture-Based Open World Face Recognition
Face Recognition (FR) is a challenging task, especially when dealing with unknown identities. While Open-Set Face Recognition (OSFR) assigns a single class to all unfamiliar subjects, Open-World Face Recognition (OWFR) employs an incremental approach, creating a new class for each unknown individual. Current OWFR approaches still present limitations, mainly regarding the accuracy gap to standard closed-set approaches and execution time. This paper proposes a fast and simple mixture-based OWFR algorithm that tackles the execution time issue while avoiding accuracy decay. The proposed method uses data curve representations and Universal Background Models based on Gaussian Mixture Models. Experimental results show that the proposed approach achieves competitive performance, considering accuracy and execution time, in both closed-set and open-world scenarios.