2021/05/14 by Adrian Kjærran, Kjærran, Adrian, Christian Bakke Vennerød +3
Computer Science · #Artificial Intelligence (cs.AI) #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #cs.AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2105.06746
arxiv created 2021/05/14 · openalex publication_date 2021/05/14 · arxiv updated 2021/05/17 · openalex created_date 2021/05/24 · openalex updated_date 2026/07/28
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.