Abstract
This paper proposes an algorithm for age estimation intentionally considering the pose variation and local deformation of faces. Pose-invariant patches are extracted in face region, and they are located from the landmarks’ neighborhood in 2D image coordinate. The landmarks can be regarded as the projections of the points on 3D face model, and the projection parameters are estimated by Convolution Neural Network (CNN). Two different structures of CNN are designed for age estimation task. One way is to stack individual patch in the spatial domain, and the stacked image is given to a CNN to make the estimation. The second is to design CNN for each particular patch and CNNs for different patches do not share weights. Together with another CNN trained on the original face region, the three streams for age estimation are combined by late fusion of the output layer. Experiments show that the proposed scheme outperforms other state-of-the-art methods.
This work was supported in part by the National Natural Science Foundation of China under Project 61302125, 61671376 and in part by Natural Science Foundation of Shanghai under Project 17ZR1408500.
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Sun, L., Qiu, S., Li, Q., Liu, H., Zhou, M. (2018). Age Estimation via Pose-Invariant 3D Face Alignment Feature in 3 Streams of CNN. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_17
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