Deep Learning with PCANet for Human Age Estimation

Abstract

Human age, as an important personal feature, has attracted great attention. Age estimation has also been considered as complex problem, how to get distinct age trait is important. In this paper, we investigate deep learning techniques for age estimation based on the PCANet, name DLPCANet. A new framework for age feature extraction based on the DLPCANet model. Different from the traditional deep learning network, we use PCA (Principal Component Analysis, PCA) algorithmic to get the filter kernels of convolutional layer instead of SGD (Stochastic Gradient Descent, SGD). Therefore, the model parameters are significantly reduced and training time is shorter. Once final feature has been fetched, we K-SVR (kernel function Support Vector Regression, K-SVR) for age estimation. The experiments are conducted in two public face aging database FG-NET and MORPH, experiments show the comparative performance in age estimation tasks against state-of-the-art approaches. In addition, the proposed method reported 4.66 and 4.72 for MAE (Mean Absolute Error, MAE) for point age estimation using FG-NET and MORPH, respectively.