Face Image Quality Assessment
14 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Face Image Quality Assessment
PIQ23
Most implemented papers
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models
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.
Optimization-Based Improvement of Face Image Quality Assessment Techniques
lsibabnikz/optimization-based-improvement-of-fiqa-techniques
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To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process.
Double Trouble? Impact and Detection of Duplicates in Face Image Datasets
Additional steps based on face recognition and face image quality assessment models reduce false positives, and facilitate the deduplication of the face images both for intra- and inter-subject duplicate sets.
AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation
lsibabnikz/ai-kd
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To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures.
GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes
jankolf/grafiqs
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We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset.
SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
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
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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
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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
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
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.
