5 research outputs found
A Multi-Task Comparator Framework for Kinship Verification
Approaches for kinship verification often rely on cosine distances between
face identification features. However, due to gender bias inherent in these
features, it is hard to reliably predict whether two opposite-gender pairs are
related. Instead of fine tuning the feature extractor network on kinship
verification, we propose a comparator network to cope with this bias. After
concatenating both features, cascaded local expert networks extract the
information most relevant for their corresponding kinship relation. We
demonstrate that our framework is robust against this gender bias and achieves
comparable results on two tracks of the RFIW Challenge 2020. Moreover, we show
how our framework can be further extended to handle partially known or unknown
kinship relations.Comment: To be published in IEEE FG 2020 - RFIW Worksho
Recognizing Families through Images with Pretrained Encoder
Kinship verification and kinship retrieval are emerging tasks in computer
vision. Kinship verification aims at determining whether two facial images are
from related people or not, while kinship retrieval is the task of retrieving
possible related facial images to a person from a gallery of images. They
introduce unique challenges because of the hidden relations and features that
carry inherent characteristics between the facial images. We employ 3 methods,
FaceNet, Siamese VGG-Face, and a combination of FaceNet and VGG-Face models as
feature extractors, to achieve the 9th standing for kinship verification and
the 5th standing for kinship retrieval in the Recognizing Family in The Wild
2020 competition. We then further experimented using StyleGAN2 as another
encoder, with no improvement in the result.Comment: Will appear as part of RFIW2020 in the Proceedings of 2020
International Conference on Automatic Face and Gesture Recognition (IEEE
AMFG
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. Organized in conjunction with
the 15th IEEE International Conference on Automatic Face and Gesture
Recognition (FG) as a Challenge, RFIW provides a platform for publishing
original work and the gathering of experts for a discussion of the next steps.
This paper summarizes the supported tasks (i.e., kinship verification,
tri-subject verification, and search & retrieval of missing children) in the
evaluation protocols, which include the practical motivation, technical
background, data splits, metrics, and benchmark results. Furthermore, top
submissions (i.e., leader-board stats) are listed and reviewed as a high-level
analysis on the state of the problem. In the end, the purpose of this paper is
to describe the 2020 RFIW challenge, end-to-end, along with forecasts in
promising future directions.Comment: White Paper for challenge in conjunction with 15th IEEE International
Conference on Automatic Face and Gesture Recognition (FG 2020
Deep Fusion Siamese Network for Automatic Kinship Verification
Automatic kinship verification aims to determine whether some individuals
belong to the same family. It is of great research significance to help missing
persons reunite with their families. In this work, the challenging problem is
progressively addressed in two respects. First, we propose a deep siamese
network to quantify the relative similarity between two individuals. When given
two input face images, the deep siamese network extracts the features from them
and fuses these features by combining and concatenating. Then, the fused
features are fed into a fully-connected network to obtain the similarity score
between two faces, which is used to verify the kinship. To improve the
performance, a jury system is also employed for multi-model fusion. Second, two
deep siamese networks are integrated into a deep triplet network for
tri-subject (i.e., father, mother and child) kinship verification, which is
intended to decide whether a child is related to a pair of parents or not.
Specifically, the obtained similarity scores of father-child and mother-child
are weighted to generate the parent-child similarity score for kinship
verification. Recognizing Families In the Wild (RFIW) is a challenging kinship
recognition task with multiple tracks, which is based on Families in the Wild
(FIW), a large-scale and comprehensive image database for automatic kinship
recognition. The Kinship Verification (track I) and Tri-Subject Verification
(track II) are supported during the ongoing RFIW2020 Challenge. Our team
(ustc-nelslip) ranked 1st in track II, and 3rd in track I. The code is
available at https://github.com/gniknoil/FG2020-kinship.Comment: 8 pages, 8 figure
A Unified Approach to Kinship Verification
In this work, we propose a deep learning-based approach for kin verification
using a unified multi-task learning scheme where all kinship classes are
jointly learned. This allows us to better utilize small training sets that are
typical of kin verification. We introduce a novel approach for fusing the
embeddings of kin images, to avoid overfitting, which is a common issue in
training such networks. An adaptive sampling scheme is derived for the training
set images to resolve the inherent imbalance in kin verification datasets. A
thorough ablation study exemplifies the effectivity of our approach, which is
experimentally shown to outperform contemporary state-of-the-art kin
verification results when applied to the Families In the Wild, FG2018, and
FG2020 datasets