Han, H.Sonmez, E.B.2024-07-182024-07-1820239798350322972https://doi.org/10.1109/ICECCME57830.2023.10253453https://hdl.handle.net/11411/6406Aksaray University;IEEE Seccion Espana;University de La Laguna2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 -- 19 July 2023 through 21 July 2023 -- -- 192890The analysis of facial expressions is a powerful tool to decode nonverbal behavior in humans. Due to its importance, several studies have already been done in the past. However, facial expression recognition on wild and multi-label faces is under-investigated also due to the limited number of available databases. This paper fills in the current lack by challenging the RAF-ML dataset and fixing the state-of-the-art performance of 50.5% on the "single label experiment". The proposed method is also tested in a second experiment, suggested by this work, which considers only wild faces having a dominant expression. The benchmark performance for the second trial is 56.1%. The deep-learning algorithms presented in this work are described in detail to facilitate their reproduction. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessDeep LearningFacial Expression RecognitionMulti-Label ClassificationBenchmarkingCell ProliferationClassification (Of İnformation)Deep LearningLearning AlgorithmsCurrentDeep LearningFacial Expression RecognitionFacial ExpressionsMulti-Label ClassificationsMulti-LabelsNon-Verbal BehavioursState-Of-The-Art PerformanceFace RecognitionConference Object2-s2.0-8517402144710.1109/ICECCME57830.2023.10253453N/A