Face Image Quality Assessment: A Literature Survey

Schlett, Torsten; Rathgeb, Christian; Henniger, Olaf; Galbally, Javier; Fierrez, Julian; Busch, Christoph

doi:10.48550/arxiv.2009.01103

Cited by 3 publications

(3 citation statements)

References 117 publications

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“…Evaluation protocols. As done in [2,10,9,25,20], we use EVRC to evaluate the performance of FIQA methods . Moreover, we introduce Area Over Curve (AOC) to quantify the EVRC results, which is defined by…”

Section: Methodsmentioning

confidence: 99%

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

Ou¹,

Chen²,

Zhang³

et al. 2021

Preprint

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In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and the recognizability of the face image. Most previous works aim to estimate the sample-wise embedding uncertainty or pair-wise similarity as the quality score, which only considers the information from partial intra-class. However, these methods ignore the valuable information from the inter-class, which is for estimating to the recognizability of face image. In this work, we argue that a high-quality face image should be similar to its intra-class samples and dissimilar to its inter-class samples. Thus, we propose a novel unsupervised FIQA method that incorporates Similarity Distribution Distance for Face Image Quality Assessment (SDD-FIQA). Our method generates quality pseudo-labels by calculating the Wasserstein Distance (WD) between the intra-class similarity distributions and inter-class similarity distributions. With these quality pseudo-labels, we are capable of training a regression network for quality prediction. Extensive experiments on benchmark datasets demonstrate that the proposed SDD-FIQA surpasses the state-of-the-arts by an impressive margin. Meanwhile, our method shows good generalization across different recognition systems.

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Section: Methodsmentioning

confidence: 99%

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

Ou¹,

Chen²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To acquire the optimal reference image in the first stage, a technique called face quality assessment [4,26] is often employed on each detected face. Although the ideal quality score should be indicative of the face recognition performance, most of early work [1,2] estimates qualities based on human-understandable factors such as luminances, distortions and pose angles, which may not directly favor the face feature learning in the second stage.…”

Section: Introductionmentioning

confidence: 99%

MagFace: A Universal Representation for Face Recognition and Quality Assessment

Meng¹,

Zhao²,

Huang³

et al. 2021

Preprint

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The performance of face recognition system degrades when the variability of the acquired faces increases. Prior work alleviates this issue by either monitoring the face quality in pre-processing or predicting the data uncertainty along with the face feature. This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face. Under the new loss, it can be proven that the magnitude of the feature embedding monotonically increases if the subject is more likely to be recognized. In addition, Mag-Face introduces an adaptive mechanism to learn a wellstructured within-class feature distributions by pulling easy samples to class centers while pushing hard samples away. This prevents models from overfitting on noisy low-quality samples and improves face recognition in the wild. Extensive experiments conducted on face recognition, quality assessments as well as clustering demonstrate its superiority over state-of-the-arts. The code is available at https://github.com/IrvingMeng/MagFace.

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“…That reliability, a concept popularly known as "quality", refers to the ability of the input sample to be used for recognition purposes produc-1 https://github.com/uam-biometrics/FaceQvec ing accurate results [2]. Recently, with the growth of biometrics, quality assessment has become one of the research topics with the highest interest of the community as it is one of the main factors responsible for the good performance of biometric systems [17].…”

Section: Introductionmentioning

confidence: 99%

FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliance

Hernandez-Ortega¹,

Fiérrez²,

Gómez³

et al. 2021

Preprint

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In this paper we develop FaceQvec, a software component for estimating the conformity of facial images with each of the points contemplated in the ISO/IEC 19794-5, a quality standard that defines general quality guidelines for face images that would make them acceptable or unacceptable for use in official documents such as passports or ID cards. This type of tool for quality assessment can help to improve the accuracy of face recognition, as well as to identify which factors are affecting the quality of a given face image and to take actions to eliminate or reduce those factors, e.g., with postprocessing techniques or re-acquisition of the image. FaceQvec consists of the automation of 25 individual tests related to different points contemplated in the aforementioned standard, as well as other characteristics of the images that have been considered to be related to facial quality. We first include the results of the quality tests evaluated on a development dataset captured under realistic conditions. We used those results to adjust the decision threshold of each test. Then we checked again their accuracy on a evaluation database that contains new face images not seen during development. The evaluation results demonstrate the accuracy of the individual tests for checking compliance with ISO/IEC 19794-5. FaceQvec is available online 1 .

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Face Image Quality Assessment: A Literature Survey

Cited by 3 publications

References 117 publications

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

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

MagFace: A Universal Representation for Face Recognition and Quality Assessment

FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliance

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