  This paper is a part of a student project in Machine Learning at the
Norwegian University of Science and Technology. In this paper, a deep
convolutional neural network with five convolutional layers and three
fully-connected layers is presented to estimate the ages of individuals based
on images. The model is in its entirety trained from scratch, where a
combination of three different datasets is used as training data. These
datasets are the APPA dataset, UTK dataset, and the IMDB dataset. The images
were preprocessed using a proprietary face-recognition software. Our model is
evaluated on both a held-out test set, and on the Adience benchmark. On the
test set, our model achieves a categorical accuracy of 52%. On the Adience
benchmark, our model proves inferior compared with other leading models, with
an exact accuray of 30%, and an one-off accuracy of 46%. Furthermore, a script
was created, allowing users to estimate their age directly using their web
camera. The script, alongside all other code, is located in our GitHub
repository: AgeNet.
