TY - GEN
T1 - Age classification from facial images
T2 - 12th International Symposium on Visual Computing, ISVC 2016
AU - Báez-Suárez, A. B.
AU - Nikou, C.
AU - Nolazco-Flores, J. A.
AU - Kakadiaris, I. A.
N1 - Funding Information:
This work has been funded in part by the Mexican National Council for Science and Technology (CONACYT) scholarship 328083 and by the UH Hugh Roy and Lillie Cranz Cullen Endowment Fund. The authors acknowledge the use of the Maxwell/Opuntia Cluster and the support of the Center of Advanced Computing and Data Systems at the University of Houston to carry out the research presented herein. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the sponsors.
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - In the majority of the methods proposed for age classification from facial images, the preprocessing steps consist of alignment and illumination correction followed by the extraction of features, which are forwarded to a classifier to estimate the age group of the person in the image. In this work, we argue that face frontalization, which is the correction of the pitch, yaw, and roll angles of the headpose in the 3D space, should be an integral part of any such algorithm as it unveils more discriminative features. Specifically, we propose a method for age classification which integrates a frontalization algorithm before feature extraction. Numerical experiments on the widely used FGnet Aging Database confirmed the importance of face frontalization achieving an average increment in accuracy of 4.43%.
AB - In the majority of the methods proposed for age classification from facial images, the preprocessing steps consist of alignment and illumination correction followed by the extraction of features, which are forwarded to a classifier to estimate the age group of the person in the image. In this work, we argue that face frontalization, which is the correction of the pitch, yaw, and roll angles of the headpose in the 3D space, should be an integral part of any such algorithm as it unveils more discriminative features. Specifically, we propose a method for age classification which integrates a frontalization algorithm before feature extraction. Numerical experiments on the widely used FGnet Aging Database confirmed the importance of face frontalization achieving an average increment in accuracy of 4.43%.
UR - http://www.scopus.com/inward/record.url?scp=85007140054&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007140054&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-50835-1_69
DO - 10.1007/978-3-319-50835-1_69
M3 - Conference contribution
AN - SCOPUS:85007140054
SN - 9783319508344
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 769
EP - 778
BT - Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings
A2 - Bebis, George
A2 - Parvin, Bahram
A2 - Skaff, Sandra
A2 - Iwai, Daisuke
A2 - Boyle, Richard
A2 - Koracin, Darko
A2 - Porikli, Fatih
A2 - Scheidegger, Carlos
A2 - Entezari, Alireza
A2 - Min, Jianyuan
A2 - Sadagic, Amela
A2 - Isenberg, Tobias
PB - Springer-Verlag
Y2 - 12 December 2016 through 14 December 2016
ER -