Journal ArticleDOI
Design and evaluation of photometric image quality measures for effective face recognition
TLDR
A new face image quality index (FQI) is proposed that combines multiple quality measures, and classifies a face image based on this index, and conducts statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.Abstract:
The performance of an automated face recognition system can be significantly influenced by face image quality. Designing effective image quality index is necessary in order to provide real-time feedback for reducing the number of poor quality face images acquired during enrollment and authentication, thereby improving matching performance. In this study, the authors first evaluate techniques that can measure image quality factors such as contrast, brightness, sharpness, focus and illumination in the context of face recognition. Second, they determine whether using a combination of techniques for measuring each quality factor is more beneficial, in terms of face recognition performance, than using a single independent technique. Third, they propose a new face image quality index (FQI) that combines multiple quality measures, and classifies a face image based on this index. In the author's studies, they evaluate the benefit of using FQI as an alternative index to independent measures. Finally, they conduct statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.read more
Citations
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Journal ArticleDOI
Representation Learning by Rotating Your Faces
Luan Tran,Xi Yin,Xiaoming Liu +2 more
TL;DR: A Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties that demonstrate the superiority of DR-GAN over the state of the art in both learning representations and rotating large-pose face images.
Journal ArticleDOI
Image recognition with deep neural networks in presence of noise – Dealing with and taking advantage of distortions
TL;DR: Construction of the deep network based denoising filter which outperforms state-of-the-art solutions, as well as proposition of a practical method of deep neural network training with noisy patterns for improvement against the noisy test patterns.
Proceedings ArticleDOI
How Image Degradations Affect Deep CNN-Based Face Recognition?
Samil Karahan,Merve Kilinc Yildirum,Kadir Kirtac,Ferhat Sukru Rende,Gultekin Butun,Hazim Kemal Ekenel +5 more
TL;DR: Analysis of the influence of image degradations on the performance of deep CNN-based face recognition approaches using the standard LFW closed-set identification protocol 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.
Journal ArticleDOI
Learning Face Image Quality From Human Assessments
Lacey Best-Rowden,Anil K. Jain +1 more
TL;DR: This is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.
Proceedings ArticleDOI
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
TL;DR: Zhang et al. as mentioned in this paper proposed a novel concept to measure face quality based on an arbitrary face recognition model by determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated.
References
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Journal ArticleDOI
A universal image quality index
Zhou Wang,Alan C. Bovik +1 more
TL;DR: Although the new index is mathematically defined and no human visual system model is explicitly employed, experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error.
Journal ArticleDOI
From few to many: illumination cone models for face recognition under variable lighting and pose
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Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Journal ArticleDOI
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
Xiaoyang Tan,Bill Triggs +1 more
TL;DR: This work presents a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition, and improves robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources.
Journal ArticleDOI
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
TL;DR: This paper presents results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects and is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image.