Publikation
DmsFIQA: Dms-Specific Face Image Quality Assessment for In-Cabin Driver Monitoring System
Qiushi Guo; Zhiye Lin; Yuanqing Luo; Lin Luo; Jason Raphael Rambach
In: Proceedings of the 20th IEEE/CVF Computer Vision and Pattern Recognition Conference Biometrics Workshop. IEEE Computer Society Workshop on Biometrics (BIOM-2026), CVPR 2026, June 3, Denver, CO, USA, IEEE/CVF, 2026.
Zusammenfassung
Face Image Quality Assessment (FIQA) is a critical preprocessing
step for face-related applications such as face
recognition and face anti-spoofing. However, most prior
FIQA methods are developed for generic capture conditions
and do not transfer well to domain-specific settings
such as in-cabin Driver Monitoring Systems (DMS), where
strong domain shift arises from frequent occlusions, large
pose variations, challenging illumination, and partial-face
captures. In this paper, we introduce DmsFIQA, a DMSoriented
FIQA framework that fills this gap. We construct a
DMS face-quality dataset covering diverse real-world conditions
and design a two-stage annotation pipeline that minimizes
manual labeling: (i) we first obtain coarse quality estimates
via large-scale model–based automatic assessment,
and (ii) we refine the supervision by ranking images through
identity-consistent similarity between per-identity query images
and high-quality templates. We evaluate DmsFIQA on
both FIQA prediction and downstream DMS face recognition.
Experiments show that DmsFIQA produces more
fine-grained and reliable quality estimates and effectively
filters low-quality faces, leading to improved robustness of
the overall DMS recognition pipeline.