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Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task that humans are believed to be very good at, it is only natural to compare the relative performance of automated face recognition and humans when processing fully...
This study focuses on the problem of extracting consistent and accurate face bounding box annotations from crowdsourced workers. Aiming to provide benchmark datasets for facial recognition training and testing, we create a 'gold standard' set against which consolidated face bounding box annotations can be evaluated. An evaluation methodology based on scores for several features of bounding box annotations...