Deep Modeling of Human Age Guesses for Apparent Age Estimation
Date of Original Version
In this paper we propose a unique deep learning formulation of the apparent age estimation problem, using the APPA-Real dataset. APPA-Real is a dataset containing 7, 591 face images, where each image is labeled by a set of approximately 38 guesses of the facial age. All guesses are collected from human labelers. In our approach, we first generate per-image label distributions from the human guesses, and then learn label distributions with convolutional neural networks and the KL-divergence loss function. We provide comparisons to models trained with other objective functions. We achieve state-of-the-art results for apparent age estimation on the APPA-Real dataset with a mean absolute error of 3.688, outperforming other methods using the same dataset.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Rondeau, Jared, and Marco Alvarez. "Deep Modeling of Human Age Guesses for Apparent Age Estimation." Proceedings of the International Joint Conference on Neural Networks 2018-July, (2018). doi: 10.1109/IJCNN.2018.8489570.