Age classification from facial images: Is frontalization necessary?

A. B. Báez-Suárez, C. Nikou, J. A. Nolazco-Flores, I. A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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%.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings
EditorsGeorge Bebis, Bahram Parvin, Sandra Skaff, Daisuke Iwai, Richard Boyle, Darko Koracin, Fatih Porikli, Carlos Scheidegger, Alireza Entezari, Jianyuan Min, Amela Sadagic, Tobias Isenberg
Number of pages10
ISBN (Print)9783319508344
StatePublished - 2016
Event12th International Symposium on Visual Computing, ISVC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10072 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Symposium on Visual Computing, ISVC 2016
Country/TerritoryUnited States
CityLas Vegas

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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