arXiv:2312.15102
공식 코드 · Papers with Code
Robust Sclera Segmentation for Skin-tone Agnostic Face Image Quality Assessment
원문 초록
Face image quality assessment (FIQA) is crucial for obtaining good face recognition performance. FIQA algorithms should be robust and insensitive to demographic factors. The eye sclera has a consistent whitish color in all humans regardless of their age, ethnicity and skin-tone. This work proposes a robust sclera segmentation method that is suitable for face images in the enrolment and the border control face recognition scenarios. It shows how the statistical analysis of the sclera pixels produces features that are invariant to skin-tone, age and ethnicity and thus can be incorporated into FIQA algorithms to make them agnostic to demographic factors.
함께 살펴볼 논문
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Deep Learning-based Compression Detection for explainable Face Image Quality Assessment
Eye Sclera for Fair Face Image Quality Assessment
Radial Distortion in Face Images: Detection and Impact
FaceOracle: Chat with a Face Image Oracle
Impact of Face Alignment on Face Image Quality
SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples
토론
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