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Multi-layer Age Regression for Face Age Estimation

Choon-Ching Ng, Yi-Tseng Cheng, Gee-Sern Hsu, Moi Hoon Yap
IAPR International Conference on Machine Vision Applications (MVA), 2017, 2017.05

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).

Link: https://ieeexplore.ieee.org/document/7986859

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