Facial age estimation using tensor based subspace learning and deep random forests

O. Guehairia (1) , F. Dornaika (2, 3, 4) , A. Ouamane (1) , Abdelmalik Taleb-Ahmed (5, 6)

1 University of Biskra Mohamed Khider
2 UPV / EHU - University of the Basque Country = Euskal Herriko Unibertsitatea
3 Henan University
4 Ikerbasque - Basque Foundation for Science
5 IEMN - Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520
6 COMNUM - IEMN - COMmunications NUMériques - IEMN

Recently, the estimation of facial age has attracted much attention. This letter extends and improves a recently developed method (Guehairia et al., 2020) for fusing multiple deep facial features for age estimation. This method was based on deep random forests. We propose a new pipeline that integrates tensor-based subspace learning before applying DRFs. Deep face features of a training set are represented as a 3D tensor. Multi-linear Whitened Principal Component (MWPCA) and Tensor Exponential Discriminant (TEDA) are used to extract the most discriminative information. The tensor subspace features are then fed into DRFs to predict age. Experiments conducted on five public face databases show that our method can compete with many state-of-the-art methods.

Mots clés: Age estimation; Deep features; Multi-linear whitened principal component; Tensor exponential discriminant analysis; Tensor based subspace; Deep random forests

Domaines: Sciences de l'ingénieur [physics]

O. Guehairia, F. Dornaika, A. Ouamane, Abdelmalik Taleb-Ahmed. Facial age estimation using tensor based subspace learning and deep random forests. Information Sciences, 2022, 609, pp.1309-1317. ⟨10.1016/j.ins.2022.07.135⟩. ⟨hal-03752059⟩