Authors:
Baiqiang Xia
1
;
Boulbaba Ben Amor
2
;
Mohamed Daoudi
2
and
Hassen Drira
2
Affiliations:
1
University Lille1 and LIFL, France
;
2
Institut Mines-Telecom/Telecom Lille and LIFL, France
Keyword(s):
Age Estimation, 3D Face, Dense Scalar Field, Random Forest Regression.
Abstract:
Age reflects the continuous accumulation of durable effects from the past since birth. Human faces deform
with time non-inversely and thus contains their aging information. In addition to its richness with anatomy
information, 3D shape of faces could have the advantage of less dependent on pose and independent of illumination,
while it hasn’t been noticed in literature. Thus, in this work we investigate the age estimation
problem from 3D shape of the face. With several descriptions grounding on Riemannian shape analysis of facial
curves, we first extracted features from ideas of face Averageness, face Symmetry, its shape variations with
Spatial and Gradient descriptors. Then, using the Random Forest-based Regression, experiments are carried
out following the Leaving-One-Person-Out (LOPO) protocol on the FRGCv2 dataset. The proposed approach
performs with a Mean Absolute Error (MAE) of 3:29 years using a gender-general test protocol. Finally, with
the gender-specific experiments, whic
h first separate the 3D scans into Female and Male subsets, then train
and test on each gender specific subset in LOPO fashion, we improves the MAE to 3:15 years, which confirms
the idea that the aging effect differs with gender.
(More)