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

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 features estimation. was based on random forests. We propose new pipeline that integrates tensor-based subspace learning before applying DRFs. Deep face training set are represented as 3D tensor. Multi-linear Whitened Principal Component (MWPCA) Tensor Exponential Discriminant (TEDA) used to extract most discriminative information. The tensor then fed into DRFs predict age. Experiments conducted five public databases show our can compete with many state-of-the-art methods.