@inproceedings{cc91b0b439aa4054b49a3630005bbdd3,
title = "Unconstrained Age Estimation with Deep Convolutional Neural Networks",
abstract = "We propose an approach for age estimation from unconstrained images based on deep convolutional neural networks (DCNN). Our method consists of four steps: face detection, face alignment, DCNN-based feature extraction and neural network regression for age estimation. The proposed approach exploits two insights: (1) Features obtained from DCNN trained for face-identification task can be used for age estimation. (2) The three-layer neural network regression method trained on Gaussian loss performs better than traditional regression methods for apparent age estimation. Our method is evaluated on the apparent age estimation challenge developed for the ICCV 2015 ChaLearn Looking at People Challenge for which it achieves the error of 0:373.",
keywords = "Estimation, Face, Face detection, Geometry, Manifolds, Neural networks, Shape",
author = "Rajeev Ranjan and Sabrina Zhou and Chen, \{Jun Cheng\} and Amit Kumar and Azadeh Alavi and Patel, \{Vishal M.\} and Rama Chellappa",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015 ; Conference date: 11-12-2015 Through 18-12-2015",
year = "2016",
month = feb,
day = "11",
doi = "10.1109/ICCVW.2015.54",
language = "English (US)",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "351--359",
booktitle = "Proceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015",
}