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Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation provided in the training data. Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset. Repeatability of data is another challenge as it is not possible to exactly recreate 'real-world' acquisition conditionsarXiv:2006.11757v1 fatcat:njk7mbtmszc3tjqsq3gxa6q6fm