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.
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Acknowledgment
This work was supported by the Gran t of the National Science Foundation of China (No. 61175121), the Program for New Century Excellent Talents in University (No. NCET-10-0117), the Grant of the National Science Foundation of Fujian Province (No. 2013J06014), the Program for Excellent Youth Talents in University of Fujian Province (No. JA10006), the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (No. ZQN-YX108), the National Natural Science Foundation of China (61502183), the Scientific Research Funds of Huaqiao University (600005-Z15Y0016), and Subsidized Project for Cultivating Postgraduates’ Innovative Ability in Scientific Research of Huaqiao University (No. 1400214009, 1400214003).
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Zheng, D., Du, J., Fan, W., Wang, J., Zhai, C. (2016). Deep Learning with PCANet for Human Age Estimation. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_26
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DOI: https://doi.org/10.1007/978-3-319-42294-7_26
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