Abstract
Age and gender estimation using face images is an exciting task in the field of computer vision. The traits from the face images are used to determine age, gender, ethnic background and emotion of people. By learning representations through the use of Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), a significant increase in performance can be obtained on these tasks, respect to the commonly used hand-crafted methodologies. This paper presents a comparative overview of the state-of-the-art approaches which estimate age and gender from human faces, some of them proposing novel network architectures or the addition of new components to already known models.
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Di Mascio, T., Fantozzi, P., Laura, L., Rughetti, V. (2022). Age and Gender (Face) Recognition: A Brief Survey. In: De la Prieta, F., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 11th International Conference. MIS4TEL 2021. Lecture Notes in Networks and Systems, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-030-86618-1_11
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