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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...
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...
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 state of-the-art, but also predict - based on someone's known preferences...
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...
We propose structured output SVM for predicting the apparent age as well as gender and smile from a single face image represented by deep features. We pose the problem of apparent age estimation as an instance of the multi-class structured output SVM classifier followed by a softmax expected value refinement. The gender and smile predictions are treated as binary classification problems. The proposed...
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...
In this paper we tackle the classification of cultural events from a single image with a deep learning based method. We use convolutional neural networks (CNNs) with VGG-16 architecture [17], pretrained on ImageNet or the Places205 dataset for image classification, and fine-tuned on cultural events data. CNN features are robustly extracted at 4 different layers in each image. At each layer Linear...
Lossy image compression allows for large storage savings but at the cost of reduced fidelity of the compressed images. There is a fair amount of literature aiming at restoration by suppressing the compression artifacts. Very recently a learned semi-local Gaussian Processes-based solution (SLGP) has been proposed with impressive results. However, when applied to top compression schemes such as JPEG...
Fashion is a major segment in e-commerce with growing importance and a steadily increasing number of products. Since manual annotation of apparel items is very tedious, the product databases need to be organized automatically, e.g. by image classification. Common image classification approaches are based on features engineered for general purposes which perform poorly on specific images of apparel...
It is well known that the accuracy of classifiers strongly depends on the distribution of the data. Consequently, a versatile classifier with a broad range of design parameters is better able to cope with various scenarios encountered in real-world applications. Kung [1] [2] [3] presented such a classifier named Ridge-SVM which incorporates the advantages of both Kernel Ridge Regression and Support...
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