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Human age provides key demographic information. It is also considered an important soft biometric trait for human identification or search. Compared to other pattern recognition problems (e.g., object classification, scene categorization), age estimation is much more challenging since the difference between facial images with age variations can be more subtle and the process of aging varies greatly among different individuals. In this work, we investigate deep learning techniques for age estimation based on the convolutional neural network (CNN). A new framework for age feature extraction based on the deep learning model is built. Compared to previous models based on CNN, we use feature maps obtained in different layers for our estimation work instead of using the feature obtained at the top layer. Additionally, a manifold learning algorithm is incorporated in the proposed scheme and this improves the performance significantly. Furthermore, we also evaluate different classification and regression schemes in estimating age using the deep learned aging pattern (DLA). To the best of our knowledge, this is the first time that deep learning technique is introduced and applied to solve the age estimation problem. Experimental results on two datasets show that the proposed approach is significantly better than the state-ofthe-art.

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Since the terrain slope cannot be neglected for forest height inversion with polarimetric synthetic aperture radar interferometry (PolInSAR), a sloped random volume over ground (S-RVoG) model is proposed to correct the terrain distortion for forest parameters estimation. A significant model complexity reduction is achieved by aligning the reference frame along the local terrain slope and changing the corresponding radar geometrical configuration. The proposed S-RVoG model inversion promises to provide much more accurate estimation of forest parameters and is validated with L-band PolInSAR data produced by PolSARpro software developed by the European Space Agency (ESA).