Human faces in surveillance videos often suffer from severe image blur, dramatic pose
variations, and occlusion. In this paper, we propose a comprehensive framework based on …

Low-resolution face recognition (LRFR) has received increasing attention over the past few
years. Its applications lie widely in the real-world environment when high-resolution or high …

Just like many other topics in computer vision, image classification has achieved significant
progress recently by using deep learning neural networks, especially the Convolutional …

Cybersecurity in front of attacks to a face recognition system is an emerging issue in the
cloud era, especially due to its strong bonds with the privacy of the users registered to the …

We demonstrate that modern image recognition methods based on artificial neural networks
can recover hidden information from images protected by various forms of obfuscation. The …

As we shift more of our lives into the virtual domain, the volume of data shared on the web
keeps increasing and presents a threat to our privacy. This works contributes to the …

In this paper, we propose a new theory of invariants to Gaussian blur. We introduce a notion
of a primordial image as a canonical form of all Gaussian blur-equivalent images. The …

Despite the great advances in visual recognition, it has been witnessed that recognition
models trained on clean images of common datasets are not robust against distorted images …

Computer vision can lead to privacy issues such as unauthorized disclosure of private
information and identity theft, but it may also be used to preserve user privacy. For example …

In wearable-based human activity recognition (HAR) research, one of the major challenges
is the large intra-class variability problem. The collected activity signal is often, if not always …