TY - GEN
T1 - Predicting privileged information for height estimation
AU - Sarafianos, Nikolaos
AU - Nikou, Christophoros
AU - Kakadiaris, Ioannis A.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology. The actual values of the anthropometric measurements are difficult to estimate accurately using state-of-the-art computer vision algorithms. Hence, we use ratios of anthropometric measurements as features. Since many anthropometric measurements are not available at test time in real-life scenarios, we employ a learning using privileged information (LUPI) framework in a regression setup. Instead of using the LUPI paradigm for regression in its original form (i.e., ϵ-SVR+), we train regression models that predict the privileged information at test time. The predictions are then used, along with observable features, to perform height estimation. Once the height is estimated, a mapping to classes is performed. We demonstrate that the proposed approach can estimate the height better and faster than the ϵ-SVR+ algorithm and report results for different genders and quartiles of humans.
AB - In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology. The actual values of the anthropometric measurements are difficult to estimate accurately using state-of-the-art computer vision algorithms. Hence, we use ratios of anthropometric measurements as features. Since many anthropometric measurements are not available at test time in real-life scenarios, we employ a learning using privileged information (LUPI) framework in a regression setup. Instead of using the LUPI paradigm for regression in its original form (i.e., ϵ-SVR+), we train regression models that predict the privileged information at test time. The predictions are then used, along with observable features, to perform height estimation. Once the height is estimated, a mapping to classes is performed. We demonstrate that the proposed approach can estimate the height better and faster than the ϵ-SVR+ algorithm and report results for different genders and quartiles of humans.
UR - http://www.scopus.com/inward/record.url?scp=85019148902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019148902&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2016.7900113
DO - 10.1109/ICPR.2016.7900113
M3 - Conference contribution
AN - SCOPUS:85019148902
T3 - Proceedings - International Conference on Pattern Recognition
SP - 3115
EP - 3120
BT - 2016 23rd International Conference on Pattern Recognition, ICPR 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Conference on Pattern Recognition, ICPR 2016
Y2 - 4 December 2016 through 8 December 2016
ER -