Impact of Face Image Quality Estimation on Presentation Attack Detection
- URL: http://arxiv.org/abs/2209.15489v1
- Date: Fri, 30 Sep 2022 14:23:47 GMT
- Title: Impact of Face Image Quality Estimation on Presentation Attack Detection
- Authors: Carlos Aravena, Diego Pasmino, Juan E. Tapia, and Christoph Busch
- Abstract summary: We study the effect of quality assessment methods on filtering bona fide and attack samples.
We show that a reduction of 20% of the training dataset by removing lower quality samples allowed us to improve the BPCER by 3% in a cross-dataset test.
- Score: 10.832111751830272
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Non-referential face image quality assessment methods have gained popularity
as a pre-filtering step on face recognition systems. In most of them, the
quality score is usually designed with face matching in mind. However, a small
amount of work has been done on measuring their impact and usefulness on
Presentation Attack Detection (PAD). In this paper, we study the effect of
quality assessment methods on filtering bona fide and attack samples, their
impact on PAD systems, and how the performance of such systems is improved when
training on a filtered (by quality) dataset. On a Vision Transformer PAD
algorithm, a reduction of 20% of the training dataset by removing lower quality
samples allowed us to improve the BPCER by 3% in a cross-dataset test.
Related papers
- Rank-based No-reference Quality Assessment for Face Swapping [88.53827937914038]
The metric of measuring the quality in most face swapping methods relies on several distances between the manipulated images and the source image.
We present a novel no-reference image quality assessment (NR-IQA) method specifically designed for face swapping.
arXiv Detail & Related papers (2024-06-04T01:36:29Z) - DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild [54.139923409101044]
We propose a novel IQA method called diffusion priors-based IQA (DP-IQA)
We use pre-trained stable diffusion as the backbone, extract multi-level features from the denoising U-Net, and decode them to estimate the image quality score.
We distill the knowledge in the above model into a CNN-based student model, significantly reducing the parameter to enhance applicability.
arXiv Detail & Related papers (2024-05-30T12:32:35Z) - GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes [9.170455788675836]
Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems.
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.
arXiv Detail & Related papers (2024-04-18T14:07:08Z) - Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment [49.36799270585947]
No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference.
We propose a novel contrastive pre-training framework tailored for PCQA (CoPA)
Our method outperforms the state-of-the-art PCQA methods on popular benchmarks.
arXiv Detail & Related papers (2024-03-15T07:16:07Z) - Test Time Adaptation for Blind Image Quality Assessment [20.50795362928567]
We introduce two novel quality-relevant auxiliary tasks at the batch and sample levels to enable TTA for blind IQA.
Our experiments reveal that even using a small batch of images from the test distribution helps achieve significant improvement in performance.
arXiv Detail & Related papers (2023-07-27T09:43:06Z) - A Quality Aware Sample-to-Sample Comparison for Face Recognition [13.96448286983864]
This work integrates a quality-aware learning process at the sample level into the classification training paradigm (QAFace)
Our method adaptively finds and assigns more attention to the recognizable low-quality samples in the training datasets.
arXiv Detail & Related papers (2023-06-06T20:28:04Z) - DifFIQA: Face Image Quality Assessment Using Denoising Diffusion
Probabilistic Models [1.217503190366097]
Face image quality assessment (FIQA) techniques aim to mitigate these performance degradations.
We present a powerful new FIQA approach, named DifFIQA, which relies on denoising diffusion probabilistic models (DDPM)
Because the diffusion-based perturbations are computationally expensive, we also distill the knowledge encoded in DifFIQA into a regression-based quality predictor, called DifFIQA(R)
arXiv Detail & Related papers (2023-05-09T21:03:13Z) - Iterative Optimization of Pseudo Ground-Truth Face Image Quality Labels [0.0]
Face image quality assessment (FIQA) techniques provide sample quality information that can be used to reject poor quality data.
We propose a quality label optimization approach, which incorporates sample-quality information from mated-pair similarities into quality predictions.
We evaluate the proposed approach using three state-of-the-art FIQA methods over three diverse datasets.
arXiv Detail & Related papers (2022-08-31T08:24:09Z) - One-Class Knowledge Distillation for Face Presentation Attack Detection [53.30584138746973]
This paper introduces a teacher-student framework to improve the cross-domain performance of face PAD with one-class domain adaptation.
Student networks are trained to mimic the teacher network and learn similar representations for genuine face samples of the target domain.
In the test phase, the similarity score between the representations of the teacher and student networks is used to distinguish attacks from genuine ones.
arXiv Detail & Related papers (2022-05-08T06:20:59Z) - AdaFace: Quality Adaptive Margin for Face Recognition [56.99208144386127]
We introduce another aspect of adaptiveness in the loss function, namely the image quality.
We propose a new loss function that emphasizes samples of different difficulties based on their image quality.
Our method, AdaFace, improves the face recognition performance over the state-of-the-art (SoTA) on four datasets.
arXiv Detail & Related papers (2022-04-03T01:23:41Z) - Inducing Predictive Uncertainty Estimation for Face Recognition [102.58180557181643]
We propose a method for generating image quality training data automatically from'mated-pairs' of face images.
We use the generated data to train a lightweight Predictive Confidence Network, termed as PCNet, for estimating the confidence score of a face image.
arXiv Detail & Related papers (2020-09-01T17:52:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.