Mahmoud Afifi

TL;DR: In this paper, the combination of isolated facial components and a contextual feature called foggy face is used to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class.

TL;DR: This paper introduces a k-nearest neighbor strategy that is able to compute a nonlinear color mapping function to correct the image's colors and shows the method is highly effective and generalizes well to camera models not in the training set.

TL;DR: Zhang et al. as mentioned in this paper proposed a two-stream convolutional neural network (CNN) which accepts hand images as input and predicts gender information from these hand images, which is then used as a feature extractor to feed a set of support vector machine classifiers for biometric identification.

TL;DR: A deep neural network (DNN) architecture trained in an end-to-end manner to learn the correct white balance for sRGB images that are rendered with the incorrect white balance is introduced.

TL;DR: This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results, based on the SIDD benchmark.