TU Darmstadt / ULB / TUbiblio

A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics

Fu, Biying ; Chen, Cong ; Henniger, Olaf ; Damer, Naser (2022)
A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics.
IEEE/CVF Winter Conference on Applications of Computer Vision. virtual Conference (04.-08.01.2022)
doi: 10.1109/WACV51458.2022.00119
Conference or Workshop Item, Bibliographie

Abstract

Quality scores provide a measure to evaluate the utility of biometric samples for biometric recognition. Biometric recognition systems require high-quality samples to achieve optimal performance. This paper focuses on face images and the measurement of face image utility with general and face-specific image quality metrics. While face-specific metrics rely on features of aligned face images, general image quality metrics can be used on the global image and relate to human perceptions. In this paper, we analyze the gap between the general image quality metrics and the face image quality metrics. Our contribution lies in a thorough examination of how different the image quality assessment algorithms relate to the utility for the face recognition task. The results of image quality assessment algorithms are further compared with those of dedicated face image quality assessment algorithms. In total, 25 different quality metrics are evaluated on three face image databases, BioSecure, LFW, and VGGFace2 using three open-source face recognition solutions, SphereFace, ArcFace, and FaceNet. Our results reveal a clear correlation between learned image metrics to face image utility even without being specifically trained as a face utility measure. Individual handcrafted features lack general stability and perform significantly worse than general face-specific quality metrics. We additionally provide a visual insight into the image areas contributing to the quality score of a selected set of quality assessment methods.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Fu, Biying ; Chen, Cong ; Henniger, Olaf ; Damer, Naser
Type of entry: Bibliographie
Title: A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics
Language: English
Date: 15 February 2022
Publisher: IEEE
Book Title: Proceedings: 2022 IEEE Winter Conference on Applications of Computer Vision: WACV 2022
Event Title: IEEE/CVF Winter Conference on Applications of Computer Vision
Event Location: virtual Conference
Event Dates: 04.-08.01.2022
DOI: 10.1109/WACV51458.2022.00119
Abstract:

Quality scores provide a measure to evaluate the utility of biometric samples for biometric recognition. Biometric recognition systems require high-quality samples to achieve optimal performance. This paper focuses on face images and the measurement of face image utility with general and face-specific image quality metrics. While face-specific metrics rely on features of aligned face images, general image quality metrics can be used on the global image and relate to human perceptions. In this paper, we analyze the gap between the general image quality metrics and the face image quality metrics. Our contribution lies in a thorough examination of how different the image quality assessment algorithms relate to the utility for the face recognition task. The results of image quality assessment algorithms are further compared with those of dedicated face image quality assessment algorithms. In total, 25 different quality metrics are evaluated on three face image databases, BioSecure, LFW, and VGGFace2 using three open-source face recognition solutions, SphereFace, ArcFace, and FaceNet. Our results reveal a clear correlation between learned image metrics to face image utility even without being specifically trained as a face utility measure. Individual handcrafted features lack general stability and perform significantly worse than general face-specific quality metrics. We additionally provide a visual insight into the image areas contributing to the quality score of a selected set of quality assessment methods.

Uncontrolled Keywords: Biometrics, Deep learning, Face recognition, Quality estimation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 03 Mar 2022 09:18
Last Modified: 03 Mar 2022 09:18
PPN:
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details