TY - GEN
T1 - Discriminating projections for estimating face age in wild images
AU - Tokola, Ryan
AU - Bolme, David
AU - Boehnen, Christopher
AU - Barstow, Del
AU - Ricanek, Karl
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/23
Y1 - 2014/12/23
N2 - Despite the fundamental variability of human appearance, the last several years have seen considerable advances in age estimation from images of faces. Many of these advances have been made possible by artificially removing external sources of variability - they focus on highly constrained images from datasets such as the MORPH face database and FG-NET. We introduce a novel approach to estimating age from a single 'wild' image, where pose, illumination, expression, face size, and face occlusions are not managed. Our method is able to reduce the effects of variations that already exist within in image. Using pose-specific projections, we map image features into a latent space that is pose-insensitive and age-discriminative. Age estimation is then performed using a multi-class SVM. We show that our approach outperforms other published results on the Images of Groups dataset [9], which is the only age-related dataset with a non-trivial number of off-axis 'wild' face images. We also show results that are competitive with recent age estimation algorithms on the mostly-frontal FG-NET dataset, and we experimentally demonstrate that our feature projections introduce insensitivity to pose.
AB - Despite the fundamental variability of human appearance, the last several years have seen considerable advances in age estimation from images of faces. Many of these advances have been made possible by artificially removing external sources of variability - they focus on highly constrained images from datasets such as the MORPH face database and FG-NET. We introduce a novel approach to estimating age from a single 'wild' image, where pose, illumination, expression, face size, and face occlusions are not managed. Our method is able to reduce the effects of variations that already exist within in image. Using pose-specific projections, we map image features into a latent space that is pose-insensitive and age-discriminative. Age estimation is then performed using a multi-class SVM. We show that our approach outperforms other published results on the Images of Groups dataset [9], which is the only age-related dataset with a non-trivial number of off-axis 'wild' face images. We also show results that are competitive with recent age estimation algorithms on the mostly-frontal FG-NET dataset, and we experimentally demonstrate that our feature projections introduce insensitivity to pose.
UR - http://www.scopus.com/inward/record.url?scp=84921743400&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2014.6996287
DO - 10.1109/BTAS.2014.6996287
M3 - Conference contribution
AN - SCOPUS:84921743400
T3 - IJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics
BT - IJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE/IAPR International Joint Conference on Biometrics, IJCB 2014
Y2 - 29 September 2014 through 2 October 2014
ER -