Face Image Quality Assessment

12 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance

Tencent/TFace CVPR 2021

Thus, we propose a novel unsupervised FIQA method that incorporates Similarity Distribution Distance for Face Image Quality Assessment (SDD-FIQA).

Pixel-Level Face Image Quality Assessment for Explainable Face Recognition

pterhoer/ExplainableFaceImageQuality 21 Oct 2021

To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model.

Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models

fbiying87/explainable_fiqa_with_amva 9 Dec 2021

To avoid the low discrimination between the general spatial activation mapping of low and high-quality images in FR models, we build our explainability tools in a higher derivative space by analyzing the variation of the FR activation maps of image sets with different quality decisions.

CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

fdbtrs/cr-fiqa CVPR 2023

Based on that, our proposed CR-FIQA uses this paradigm to estimate the face image quality of a sample by predicting its relative classifiability.

FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment

uam-biometrics/faceqgen 3 Jan 2022

This comparison shows that, even though FaceQgen does not surpass the best existing face quality assessment methods in terms of face recognition accuracy prediction, it achieves good enough results to demonstrate the potential of semi-supervised learning approaches for quality estimation (in particular, data-driven learning based on a single high quality image per subject), having the capacity to improve its performance in the future with adequate refinement of the model and the significant advantage over competing methods of not needing quality labels for its development.

FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration

lsibabnikz/faceqan 5 Dec 2022

In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent.

Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality Assessment

oufuzhao/eqbm ICCV 2023

Furthermore, we design an easy-to-hard training scheduler based on the inter-domain uncertainty and intra-domain quality margin as well as the ranking-based domain adversarial network to enhance the effectiveness of transfer learning and further reduce the source risk in domain adaptation.

Pose Impact Estimation on Face Recognition using 3D-Aware Synthetic Data with Application to Quality Assessment

datasciencegrimmer/syn-yawpitch 1 Mar 2023

Evaluating the quality of facial images is essential for operating face recognition systems with sufficient accuracy.

Considerations on the Evaluation of Biometric Quality Assessment Algorithms

dasec/dataset-duplicates 23 Mar 2023

Additionally, a discard fraction limit or range must be selected to compute pAUC values, which can then be used to quantitatively rank quality assessment algorithms.

DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models

LSIbabnikz/DifFIQA 9 May 2023

In this paper, we present a powerful new FIQA approach, named DifFIQA, which relies on denoising diffusion probabilistic models (DDPM) and ensures highly competitive results.