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Found 11 papers, 6 papers with code
The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces
Recognizing Families In the Wild (RFIW), held as a data challenge in conjunction with the 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG), is a large-scale, multi-track visual kinship recognition evaluation.
Multimodal In-bed Pose and Shape Estimation under the Blankets
Typically, humans are covered by a blanket when resting, for which we propose a multimodal approach to uncover the subjects so their bodies at rest can be viewed without the occlusion of the blankets above.
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis
Even as impressive milestones are achieved in synthesizing faces, the importance of preserving identity is needed in practice and should not be overlooked.
Families In Wild Multimedia: A Multimodal Database for Recognizing Kinship
Thus, to narrow the gap between research and reality and enhance the power of kinship recognition systems, we extend FIW with multimedia (MM) data (i. e., video, audio, and text captions).
Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making
We review the public resources and data challenges that enabled and inspired many to hone-in on the views of automatic kinship recognition in the visual domain.
Dual-Attention GAN for Large-Pose Face Frontalization
Face frontalization provides an effective and efficient way for face data augmentation and further improves the face recognition performance in extreme pose scenario.
Face Recognition: Too Bias, or Not Too Bias?
Thus, the conventional approach of learning a global threshold for all pairs resulting in performance gaps among subgroups.
Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge
Recognizing Families In the Wild (RFIW): an annual large-scale, multi-track automatic kinship recognition evaluation that supports various visual kin-based problems on scales much higher than ever before.
Joint Super-Resolution and Alignment of Tiny Faces
As for SR, the proposed method recovers sharper edges and more details from LR face images than other state-of-the-art methods, which we demonstrate qualitatively and quantitatively.
Laplace Landmark Localization
Our method claims state-of-the-art on all of the 300W benchmarks and ranks second-to-best on the Annotated Facial Landmarks in the Wild (AFLW) dataset.
Ranked #5 on
Face Alignment
on AFLW-19
(NME_box (%, Full) metric)
Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks
Motivated by the lack of a single, unified dataset for kinship recognition, we aim to provide a dataset that captivates the interest of the research community.
