How Image Degradations Affect Deep CNN-based Face Recognition?

Face recognition approaches that are based on deep convolutional neural networks (CNN) have been dominating the field. The performance improvements they have provided in the so called in-the-wild datasets are significant, however, their performance under image quality degradations have not been assessed, yet. This is particularly important, since in real-world face recognition applications, images may contain various kinds of degradations due to motion blur, noise, compression artifacts, color distortions, and occlusion. In this work, we have addressed this problem and analyzed the influence of these image degradations on the performance of deep CNN-based face recognition approaches using the standard LFW closed-set identification protocol. We have evaluated three popular deep CNN models, namely, the AlexNet, VGG-Face, and GoogLeNet. Results have indicated that blur, noise, and occlusion cause a significant decrease in performance, while deep CNN models are found to be robust to distortions, such as color distortions and change in color balance.Comment: 8 pages, 3 figure

Effect of image degradation on performance of Convolutional Neural Networks

The use of deep learning approaches in image classification and recognition tasks is growing rapidly and gaining huge importance in research due to the great enhancement they achieve. Particularly, Convolutional Neural Networks (CNN) have shown a great significance in the field of computer vision and image recognition recently. They made an enormous improvement in classification and recognition systems’ accuracy. In this work, an investigation of how image related parameters such as contrast, noise, and occlusion affect the work of CNNs is to be carried out. Also, whether all types of variations cause the same drop to performance and how they rank in that regard is considered. After the experiments were carried out, the results revealed that the extent of effect of each degradation type to be different from others. It was clear that blurring and occlusion affects accuracy more than noise when considering the root mean square error as a common objective measure of the amount of alteration that each degradation caused

Augraphy: A Data Augmentation Library for Document Images

This paper introduces Augraphy, a Python library for constructing data augmentation pipelines which produce distortions commonly seen in real-world document image datasets. Augraphy stands apart from other data augmentation tools by providing many different strategies to produce augmented versions of clean document images that appear as if they have been altered by standard office operations, such as printing, scanning, and faxing through old or dirty machines, degradation of ink over time, and handwritten markings. This paper discusses the Augraphy tool, and shows how it can be used both as a data augmentation tool for producing diverse training data for tasks such as document denoising, and also for generating challenging test data to evaluate model robustness on document image modeling tasks

On the Robustness of Face Recognition Algorithms Against Attacks and Bias

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.Comment: Accepted in Senior Member Track, AAAI202