Kinship Verification
7 papers with code • 2 benchmarks • 2 datasets
Kinship verification aims to find out whether there is a kin relation for a given pair of facial images.
Benchmarks
These leaderboards are used to track progress in Kinship Verification
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KinFaceW-I
KinFaceW-II
Most implemented papers
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.
Confidence-Calibrated Face and Kinship Verification
In this paper, we investigate the problem of prediction confidence in face and kinship verification.
Kinship Identification through Joint Learning Using Kinship Verification Ensembles
The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.
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.
ChildPredictor: A Child Face Prediction Framework with Disentangled Learning
On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors.
StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face Synthesis
CVI-SZU/StyleGene
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CVPR 2023
As cycle-like losses are designed to measure the L_2 distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i. e. no paired kinship face data is required.
KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning
garynlfd/kfc
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Kinship verification is an emerging task in computer vision with multiple potential applications.
