Multi-layer age regression for face age estimation
Choon-Ching Ng, Yi-Tseng Cheng, Gee-Sern Jison Hsu, Moi Hoon Yap · 2017
Face features convey many personal information that promote and regulate our social linkages. Age prediction using single layer estimation such as aging subspace or a hybrid pattern is limited due to the complexity of human faces. In this work, we propose Multilayer Age Regression (MAR) where the face age is predicted based on a coarse-to-fine estimation using global and local features. In the first layer, Support Vector Regression (SVR) performs a between group prediction by the parameters of Facial Appearance Model (FAM). In the second layer, a within group estimation is performed using FAM, Bio-Inspired Features (BIF), Kernel-based Local Binary Patterns (KLBP) and Multi-scale Wrinkle Patterns (MWP). The performance of MAR is assessed on four benchmark datasets: FGNET, MORPH, FERET and PAL. Results showed that MAR outperforms the state of the art on FERET with a Mean Absolute Error (MAE) of 3.00 (±4.14).