Age Estimation Using 3D Shape of the Face

  • Baiqiang Xia
  • Boulbaba Ben Amor
  • Mohamed Daoudi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 550)


The 3D shape of human faces deform with time and contain rich aging information. However, in the literature, no work has been done with the 3D shape of face for age estimation. Thus, we propose in this paper to explore the 3D facial surface in age estimation. Based on Riemannian shape analysis of facial curves, we extract four types of Dense Scalar Field (DSF) descriptions from the 3D facial surface, which reflect the face Averageness, face Symmetry, and the Spatial and Gradient deviations of the face. Experiments are carried out following the Leave-One-Person-Out (LOPO) cross-validation on the earliest 466 scans in FRGCv2 dataset, using the Random Forest Regressor. With the DSF features, the proposed approach achieves 3.29 years Mean Absolute Error (MAE) in the gender-general experiments, and 3.15 years MAE in the gender-specific experiments. Results confirm the idea that the face aging differs with gender. To address the high dimensionality of DSF features and the imbalance in training instances, we propose to use a weighted PCA method. In both the gender-general and gender-specific experiments, the age estimation performances using weighted PCA are comparable to the performances using the DSF descriptions. While, the size of feature is significantly smaller with the weighted PCA.


Age estimation 3D face Dense scalar field Principal component analysis Random forest regression 


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© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Baiqiang Xia
    • 1
  • Boulbaba Ben Amor
    • 1
  • Mohamed Daoudi
    • 1
  1. 1.Télécom Lille/LIFL (UMR CNRS 8022)Cité ScientifiqueVilleneuve D’ascqFrance

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