Face Image Reflection Removal
원문 초록
Face images captured through the glass are usually contaminated by reflections. The non-transmitted reflections make the reflection removal more challenging than for general scenes, because important facial features are completely occluded. In this paper, we propose and solve the face image reflection removal problem. We remove non-transmitted reflections by incorporating inpainting ideas into a guided reflection removal framework and recover facial features by considering various face-specific priors. We use a newly collected face reflection image dataset to train our model and compare with state-of-the-art methods. The proposed method shows advantages in estimating reflection-free face images for improving face recognition.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Attributes Shape the Embedding Space of Face Recognition Models
Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space
OpenRR-1k: A Scalable Dataset for Real-World Reflection Removal
DiffProb: Data Pruning for Face Recognition
StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation
FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images
토론
이 논문과 연결된 포럼 글이 아직 없습니다.