Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.

On the errors involved in the estimate of glacier ice volume from ice thickness data

The assessment of the glacier thickness is one of the most widespread applications of radioglaciology, and is the basis for estimating the glacier volume. The accuracy of the measurement of ice thickness, the distribution of profiles over the glacier and the accuracy of the boundary delineation of the glacier are the most important factors determining the error in the evaluation of the glacier volume. The aim of this study is to get an accurate estimate of the error incurred in the estimate of glacier volume from GPR-retrieved ice-thickness data.