Facial Age Estimation using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2105.06746v1
• Date: Fri, 14 May 2021 10:09:47 GMT
• Title: Facial Age Estimation using Convolutional Neural Networks
• Authors: Adrian Kj{\ae}rran and Christian Bakke Venner{\o}d and Erling Stray Bugge
• Abstract summary: This paper is a part of a student project in Machine Learning at the Norwegian University of Science and Technology. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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