How Image Degradations Affect Deep CNN-Based Face Recognition?

Karahan, Samil; Yildirum, Merve Kilinc; Kirtaç, Kadir; Rende, Ferhat Şükrü; Butun, Gultekin; Ekenel, Hazım Kemal

doi:10.1109/biosig.2016.7736924

Cited by 117 publications

(81 citation statements)

References 18 publications

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“…Several studies [6,7,8,9] have explored the capacity of deep-learning based FR-systems to handle variations in pose, illumination and scale, that is, challenges related to variability of face-biometric samples of a single client. Karahan et al [13] studied the 1 fragility of CNN-based FR systems when confronted with image degradations such as blurring, occlusion, compressionartifacts, and color-distortion. Robustness to such degradations is necessary for FR systems operating in relatively unconstrained environments.…”

Section: Introductionmentioning

confidence: 99%

Deeply vulnerable: a study of the robustness of face recognition to presentation attacks

2017

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The vulnerability of deep-learning based face-recognition (FR) methods, to presentation attacks (PA) is studied in this paper. Recently proposed FR methods based on deep neural networks (DNN) have been shown to outperform most other methods by a significant margin. In a trustworthy face-verification system, however, maximizing recognition-performance alone is not sufficient -the system should also be capable of resisting various kinds of attacks, including presentation-attacks (PA). Previous experience has shown that the PA-vulnerability of FR systems tends to increase with face-verification accuracy. Using several publicly available PA datasets, we show that DNN-based FR systems compensate for variability between bona fide and PA samples, and tend to score them similarly, which makes such FR systems extremely vulnerable to PAs. Experiments show the vulnerability of the studied DNN-based FR systems to be consistently higher than 90%, and often higher than 98%.

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Section: Introductionmentioning

confidence: 99%

Deeply vulnerable: a study of the robustness of face recognition to presentation attacks

2017

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“…In [135], the boundary where the face recognition performance is largely degraded is 16×16 pixels. Karahan et al [136] found Gaussian noise with its standard deviation ranging from 10 to 20 will cause a rapid performance decline. In [137], more impacts of contrast, brightness, sharpness, and out-of-focus on face recognition are analyzed.…”

Section: Visual Recognition Under Adverse Conditionsmentioning

confidence: 99%

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

Yang

Yuan

Ren

et al. 2020

IEEE Trans. on Image Process.

191

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Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

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“…In [4], the authors find the face recognition performance to be notably deteriorated when face regions become smaller than 16 × 16 pixels. [7] reports a rapid decline of face recognition accuracies, with Gaussian noise of standard deviation (std) between 10 and 20. [5], [6] reveal more impacts of contrast, brightness, sharpness, and out-of-focus on image based face recognition.…”

Section: A Visual Recognition Under Adverse Conditionsmentioning

confidence: 99%

Enhance Visual Recognition Under Adverse Conditions via Deep Networks

Liu

Cheng

Wang

et al. 2019

IEEE Trans. on Image Process.

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Visual recognition under adverse conditions is a very important and challenging problem of high practical value, due to the ubiquitous existence of quality distortions during image acquisition, transmission, or storage. While deep neural networks have been extensively exploited in the techniques of low-quality image restoration and high-quality image recognition tasks respectively, few studies have been done on the important problem of recognition from very low-quality images. This paper proposes a deep learning based framework for improving the performance of image and video recognition models under adverse conditions, using robust adverse pre-training or its aggressive variant. The robust adverse pre-training algorithms leverage the power of pre-training and generalizes conventional unsupervised pre-training and data augmentation methods. We further develop a transfer learning approach to cope with real-world datasets of unknown adverse conditions. The proposed framework is comprehensively evaluated on a number of image and video recognition benchmarks, and obtains significant performance improvements under various single or mixed adverse conditions. Our visualization and analysis further add to the explainability of results.

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How Image Degradations Affect Deep CNN-Based Face Recognition?

Cited by 117 publications

References 18 publications

Deeply vulnerable: a study of the robustness of face recognition to presentation attacks

Deeply vulnerable: a study of the robustness of face recognition to presentation attacks

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

Enhance Visual Recognition Under Adverse Conditions via Deep Networks

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