Face Image Quality Assessment: A Literature Survey

Schlett, Torsten; Rathgeb, Christian; Henniger, Olaf; Galbally, Javier; Fiérrez, Julián; Busch, Christoph

doi:10.1145/3507901

Cited by 75 publications

(47 citation statements)

References 169 publications

(309 reference statements)

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“…2. The used subset of the FRGCv2 dataset has good-quality face images which allows to analyse the sole impact of fun selfie filters on FR modules in the absence of quality-related factors [54], e.g., variations in pose or illumination.…”

Section: Fun Selfie Filter Databasementioning

confidence: 99%

Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

Botezatu¹,

Ibsen²,

Rathgeb³

et al. 2022

Preprint

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This work investigates the impact of fun selfie filters, which are frequently used to modify selfies, on face recognition systems. Based on a qualitative assessment and classification of freely available mobile applications, ten relevant fun selfie filters are selected to create a database. To this end, the selected filters are automatically applied to face images of public face image databases. Different state-of-the-art methods are used to evaluate the influence of fun selfie filters on the performance of face detection using dlib, RetinaFace, and a COTS method, sample quality estimated by FaceQNet and MagFace, and recognition accuracy employing ArcFace and a COTS algorithm. The obtained results indicate that selfie filters negatively affect face recognition modules, especially if fun selfie filters cover a large region of the face, where the mouth, nose, and eyes are covered. To mitigate such unwanted effects, a GAN-based selfie filter removal algorithm is proposed which consists of a segmentation module, a perceptual network, and a generation module. In a cross-database experiment the application of the presented selfie filter removal technique has shown to significantly improve the biometric performance of the underlying face recognition systems.

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Section: Fun Selfie Filter Databasementioning

confidence: 99%

Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

Botezatu¹,

Ibsen²,

Rathgeb³

et al. 2022

Preprint

Self Cite

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“…Face quality estimation From the results, it can be observed that the performance of FaceQnet and SER-FIQ decreases as the coverage of facial tattoos and paintings on a face become large. Since face quality estimation measures the utility of a facial image for face recognition [51], the observations are expected since for the comparison and features extraction module of both ArcFace and COTS a decrease in biometric performance was observed for facial tattoos and paintings compared to facial images without such manipulations. Feature extraction and comparison In the conducted experiments, we showed that facial tattoos and paintings significantly impact face recognition systems' ability to extract face features and recognise individuals.…”

Section: Feature Extraction and Comparisonmentioning

confidence: 99%

Impact of Facial Tattoos and Paintings on Face Recognition Systems

Ibsen,

Rathgeb,

Fink

et al. 2021

Preprint

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In the past years, face recognition technologies have shown impressive recognition performance, mainly due to recent developments in deep convolutional neural networks. Notwithstanding those improvements, several challenges which affect the performance of face recognition systems remain. In this work, we investigate the impact that facial tattoos and paintings have on current face recognition systems. To this end, we first collected an appropriate database containing image-pairs of individuals with and without facial tattoos or paintings. The assembled database was used to evaluate how facial tattoos and paintings affect the detection, quality estimation, as well as the feature extraction and comparison modules of a face recognition system. The impact on these modules was evaluated using stateof-the-art open-source and commercial systems. The obtained results show that facial tattoos and paintings affect all the tested modules, especially for images where a large area of the face is covered with tattoos or paintings. Our work is an initial casestudy and indicates a need to design algorithms which are robust to the visual changes caused by facial tattoos and paintings.

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“…Beyond the vulnerability of face recognition due to facial masks, Fang et al [11] further analysed security critical issues in terms of face presentation attacks and the detectability of these attacks when wearing a mask or when a real mask is placed on the attack. Other works focus on relating the FIQ to morphing faces [14], face parts [13], image quality [12] or in a review [30].…”

Section: Related Workmentioning

confidence: 99%

The Effect of Wearing a Face Mask on Face Image Quality

Fu¹,

Kirchbuchner²,

Damer³

2021

Preprint

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Due to the COVID-19 situation, face masks have become a main part of our daily life. Wearing mouth-and-nose protection has been made a mandate in many public places, to prevent the spread of the COVID-19 virus. However, face masks affect the performance of face recognition, since a large area of the face is covered. The effect of wearing a face mask on the different components of the face recognition system in a collaborative environment is a problem that is still to be fully studied. This work studies, for the first time, the effect of wearing a face mask on face image quality by utilising state-of-theart face image quality assessment methods of different natures. This aims at providing better understanding on the effect of face masks on the operation of face recognition as a whole system. In addition, we further studied the effect of simulated masks on face image utility in comparison to real face masks. We discuss the correlation between the mask effect on face image quality and that on the face verification performance by automatic systems and human experts, indicating a consistent trend between both factors. The evaluation is conducted on the database containing (1) no-masked faces, (2) real face masks, and (3) simulated face masks, by synthetically generating digital facial masks on no-masked faces. Finally, a visual interpretation of the face areas contributing to the quality score of a selected set of quality assessment methods is provided to give a deeper insight into the difference of network decisions in masked and non-masked faces, among other variations.

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Face Image Quality Assessment: A Literature Survey

Cited by 75 publications

References 169 publications

Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

Impact of Facial Tattoos and Paintings on Face Recognition Systems

The Effect of Wearing a Face Mask on Face Image Quality

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