This paper concerns the estimation of facial attributes – namely, age and gender – from images of faces acquired in challenging, “in the wild” conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here we address this problem by making the following contributions. First, (i), in answer to one of the key problems of age estimation research – absence of data – we offer a unique dataset of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. (ii) We describe the dropout-SVM approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to our knowledge, for the first time. Finally, (iii), we present a robust face alignment technique which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks, as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.