Deep Fusion Siamese Network for Automatic Kinship Verification

@article{Yu2020DeepFS,
  title={Deep Fusion Siamese Network for Automatic Kinship Verification},
  author={Jun Yu and Mengyan Li and Xinlong Hao and Guochen Xie},
  journal={2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)},
  year={2020},
  pages={892-899}
}
  • Jun Yu, Mengyan Li, Guochen Xie
  • Published 30 May 2020
  • Computer Science
  • 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
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… 
Visual Kinship Recognition: A Decade in the Making
TLDR
The public resources and data challenges that enabled and inspired many to hone-in on one or more views of automatic kinship recognition in the visual domain are reviewed and a stronghold for the state of progress is established for the different problems in a consistent manner.
Solving the Families In the Wild Kinship Verification Challenge by Program Synthesis
  • Junyi Huang, M. Strome, Iddo Drori
  • Psychology, Computer Science
    2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
  • 2021
TLDR
This work uses Codex to generate model variants, and also demonstrates its ability to generate entire running programs for kinship verification tasks of specific relationships, among the top 3 winning entries in the competition.
The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces
TLDR
Submissions for this year's RFIW are summarized, and the results for kinship verification, tri-subject verification, and family member search and retrieval are reviewed.
Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge.
TLDR
The purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.
Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making
TLDR
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 are reviewed and a stronghold for the state of progress for the different problems is established.
Patch-Based Dual-Tree Complex Wavelet Transform for Kinship Recognition
TLDR
Novel patch-based kinship recognition methods based on dual-tree complex wavelet transform (DT-CWT) and Selective Patch-Based DT-C WT are presented, which achieves competitive accuracy to state-of-the-art methods and representative patches contribute more similarities in parent/child image pairs and improve kinship accuracy.
Top 3 in FG 2021 Families In the Wild Kinship Verification Challenge
TLDR
This work demonstrates high quality kinship verification by participating in the 2021 Recognizing Families in the Wild challenge which provides the largest publicly available dataset in the field.
Recognizing Families In the Wild (RFIW): The 4th Edition
  • Joseph P. Robinson, Yu Yin, Y. Fu
  • Computer Science
    2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
  • 2020
TLDR
The purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.

References

SHOWING 1-10 OF 46 REFERENCES
Kinship Verification using Deep Siamese Convolutional Neural Network
TLDR
A deep learning approach is presented using Siamese Convolutional Neural Network Architecture to quantify the similarity between two given photos, and uses different similarity metric such as L1 norm, L2 Norm, and Cosine Similarity.
KinNet: Fine-to-Coarse Deep Metric Learning for Kinship Verification
TLDR
In this work, KinNet is proposed, a fine-to-coarse deep metric learning framework for kinship verification, which transfers knowledge from the large-scale-data-driven face recognition task by pre-training the network with massive data for face recognition.
Kinship Verification on Families in the Wild with Marginalized Denoising Metric Learning
TLDR
This work proposes a denoising auto-encoder based robust metric learning (DML) framework and its marginalized version (mD ML) to explicitly preserve the intrinsic structure of data and simultaneously endow the discriminative information into the learned features.
Neighborhood repulsed metric learning for kinship verification
TLDR
This paper proposes a new neighborhood repulsed metric learning (NRML) method for kinship verification, and proposes a multiview NRM-L method to seek a common distance metric to make better use of multiple feature descriptors to further improve the verification performance.
AdvNet: Adversarial Contrastive Residual Net for 1 Million Kinship Recognition
TLDR
Inspired by maximum mean discrepancy (MMD) and generative adversarial net (GAN), family ID based Adversarial contrastive residual Network (AdvNet) is proposed for large-scale kinship recognition in this paper and shows the effectiveness of the proposed AdvNet and ensemble based feature augmentation.
Kinship Verification through Transfer Learning
TLDR
Experimental results show that the hypothesis on the role of young parents is valid and transfer learning is effective to enhance the verification accuracy and the large gap between distributions can be significantly reduced and kinship verification problem becomesmore discriminative.
Discriminative Deep Metric Learning for Face and Kinship Verification
TLDR
A discriminative deep multi-metric learning method to jointly learn multiple neural networks, under which the correlation of different features of each sample is maximized, and the distance of each positive pair is reduced and that of each negative pair is enlarged.
Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks
TLDR
This work presents the largest kinship recognition dataset to date, Families in the Wild, and demonstrates that a pre-trained Convolutional Neural Network as an off-the-shelf feature extractor outperforms the other feature types.
Graph-Based Kinship Recognition
TLDR
This work proposes a graph-based approach that incorporates facial similarities between all family members in a photograph in order to improve the performance of kinship recognition and introduces a database of group photographs with kinship annotations.
...
...