In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pretrained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-ofthe-art results for both real and apparent age estimation.

In this paper we tackle the estimation of apparent age in still face images with deep learning. Our convolutional neural networks (CNNs) use the VGG-16 architecture [13] and are pretrained on ImageNet for image classification. In addition, due to the limited number of apparent age annotated images, we explore the benefit of finetuning over crawled Internet face images with available age. We crawled 0.5 million images of celebrities from IMDB and Wikipedia that we make public. This is the largest public dataset for age prediction to date. We pose the age regression problem as a deep classification problem followed by a softmax expected value refinement and show improvements over direct regression training of CNNs. Our proposed method, Deep EXpectation (DEX) of apparent age, first detects the face in the test image and then extracts the CNN predictions from an ensemble of 20 networks on the cropped face. The CNNs of DEX were finetuned on the crawled images and then on the provided images with apparent age annotations. DEX does not use explicit facial landmarks. Our DEX is the winner (1 st place) of the ChaLearn LAP 2015 challenge on apparent age estimation with 115 registered teams, significantly outperforming the human reference.

In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning.We validate our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and methods (i.e. A+, SR-CNN, ANR, Zeyde, Yang) and achieve substantial improvements. The techniques are widely applicable and require no changes or only minor adjustments of the SR methods.Moreover, our Improved A+ (IA) method sets new stateof-the-art results outperforming A+ by up to 0.9dB on average PSNR whilst maintaining a low time complexity.

Environment (RIVM) NO 2 lidar. We show that NO 2 from Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) compares well with in situ measurements. We show that different MAX-DOAS instruments, operating simultaneously during the campaign, give very similar results. We also provide unique information on the spatial homogeneity and the vertical and temporal variability of NO 2 , showing that during a number of days, the NO 2 columns derived from measurements in different directions varied significantly, which implies that, under polluted conditions, measurements in one single azimuth direction are not always representative for the averaged field that the satellite observes. In addition, we show that there is good agreement between tropospheric NO 2 from OMI and MAX-DOAS, and also between total NO 2 from OMI and directsun observations. Observations of the aerosol optical thickness (AOT) show that values derived with three ground-based instruments correspond well with each other, and with aerosol optical thicknesses observed by OMI.

Abstract. Non-maximum suppression (NMS) is a key post-processing step in many computer vision applications. In the context of object detection, it is used to transform a smooth response map that triggers many imprecise object window hypotheses in, ideally, a single bounding-box for each detected object. The most common approach for NMS for object detection is a greedy, locally optimal strategy with several hand-designed components (e.g., thresholds). Such a strategy inherently suffers from several shortcomings, such as the inability to detect nearby objects. In this paper, we try to alleviate these problems and explore a novel formulation of NMS as a well-defined clustering problem. Our method builds on the recent Affinity Propagation Clustering algorithm, which passes messages between data points to identify cluster exemplars. Contrary to the greedy approach, our method is solved globally and its parameters can be automatically learned from training data. In experiments, we show in two contexts -object class and generic object detection -that it provides a promising solution to the shortcomings of the greedy NMS.

This observational study aimed to use artificial intelligence to describe the impact of orthognathic treatment on facial attractiveness and age appearance. Pre- and post-treatment photographs (n=2164) of 146 consecutive orthognathic patients were collected for this longitudinal retrospective single-centre study. Every image was annotated with patient-related data (age; sex; malocclusion; performed surgery). For every image, facial attractiveness (score: 0-100) and apparent age were established with dedicated convolutional neural networks trained on >0.5million images for age estimation and with >17million ratings for attractiveness. Results for pre- and post-treatment photographs were averaged for every patient separately, and apparent age compared to real age (appearance). Changes in appearance and facial attractiveness were statistically examined. Analyses were performed on the entire sample and subgroups (sex; malocclusion; performed surgery). According to the algorithms, most patients' appearance improved with treatment (66.4%), resulting in younger appearance of nearly 1year [mean change: -0.93years (95% confidence interval (CI): -1.50; -0.36); p=0.002), especially after profile-altering surgery. Orthognathic treatment had similarly a beneficial effect on attractiveness in 74.7% [mean difference: 1.22 (95% CI: 0.81; 1.63); p<0.001], especially after lower jaw surgery. This investigation illustrates that artificial intelligence might be considered to score facial attractiveness and apparent age in orthognathic patients.

After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods. The estimation of "apparent age" is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks.

For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already pointed out that age, gender, and average attractiveness can be estimated with reasonable precision. We improve the stateof-the-art, but also predict -based on someone's known preferences -how much that particular person is attracted to a novel face. Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and -as the main novelties -visual regularized collaborative filtering to infer interperson preferences as well as a novel regression technique for handling visual queries without rating history. We validate the method using a very large dataset from a dating site as well as images from celebrities. Our experiments yield convincing results, i.e. we predict 76% of the ratings correctly solely based on an image, and reveal some sociologically relevant conclusions. We also validate our collaborative filtering solution on the standard MovieLens rating dataset, augmented with movie posters, to predict an individuals movie rating. We demonstrate our algorithms on howhot.io which went viral around the Internet with more than 50 million pictures evaluated in the first month.