16 research outputs found
Challenge report: Recognizing Families In the Wild Data Challenge
This paper is a brief report to our submission to the Recognizing Families In
the Wild Data Challenge (4th Edition), in conjunction with FG 2020 Forum.
Automatic kinship recognition has attracted many researchers' attention for its
full application, but it is still a very challenging task because of the
limited information that can be used to determine whether a pair of faces are
blood relatives or not. In this paper, we studied previous methods and proposed
our method. We try many methods, like deep metric learning-based, to extract
deep embedding feature for every image, then determine if they are blood
relatives by Euclidean distance or method based on classes. Finally, we find
some tricks like sampling more negative samples and high resolution that can
help get better performance. Moreover, we proposed a symmetric network with a
binary classification based method to get our best score in all tasks.Comment: RFIW,IEEE FG202
SelfKin: Self Adjusted Deep Model For Kinship Verification
One of the unsolved challenges in the field of biometrics and face
recognition is Kinship Verification. This problem aims to understand if two
people are family-related and how (sisters, brothers, etc.) Solving this
problem can give rise to varied tasks and applications. In the area of homeland
security (HLS) it is crucial to auto-detect if the person questioned is related
to a wanted suspect, In the field of biometrics, kinship-verification can help
to discriminate between families by photos and in the field of predicting or
fashion it can help to predict an older or younger model of people faces.
Lately, and with the advanced deep learning technology, this problem has gained
focus from the research community in matters of data and research. In this
article, we propose using a Deep Learning approach for solving the
Kinship-Verification problem. Further, we offer a novel self-learning deep
model, which learns the essential features from different faces. We show that
our model wins the Recognize Families In the Wild(RFIW2018,FG2018) challenge
and obtains state-of-the-art results. Moreover, we show that our proposed model
can reduce the size of the network by half without loss in performance
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
Achieving Better Kinship Recognition Through Better Baseline
Recognizing blood relations using face images can be seen as an application
of face recognition systems with additional restrictions. These restrictions
proved to be difficult to deal with, however, recent advancements in face
verification show that there is still much to gain using more data and novel
ideas. As a result face recognition is a great source domain from which we can
transfer the knowledge to get better performance in kinship recognition as a
source domain. We present a new baseline for an automatic kinship recognition
task and relatives search based on RetinaFace[1] for face registration and
ArcFace[2] face verification model. With the approach described above as the
foundation, we constructed a pipeline that achieved state-of-the-art
performance on two tracks in the recent Recognizing Families In the Wild Data
Challenge.Comment: Accepted for the 4th Recognizing Families In the Wild Worksho
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
Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos
Identifying kinship relations has garnered interest due to several
applications such as organizing and tagging the enormous amount of videos being
uploaded on the Internet. Existing research in kinship verification primarily
focuses on kinship prediction with image pairs. In this research, we propose a
new deep learning framework for kinship verification in unconstrained videos
using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE). This
new autoencoder formulation introduces class-specific sparsity in the weight
matrix. The proposed three-stage SMNAE based kinship verification framework
utilizes the learned spatio-temporal representation in the video frames for
verifying kinship in a pair of videos. A new kinship video (KIVI) database of
more than 500 individuals with variations due to illumination, pose, occlusion,
ethnicity, and expression is collected for this research. It comprises a total
of 355 true kin video pairs with over 250,000 still frames. The effectiveness
of the proposed framework is demonstrated on the KIVI database and six existing
kinship databases. On the KIVI database, SMNAE yields video-based kinship
verification accuracy of 83.18% which is at least 3.2% better than existing
algorithms. The algorithm is also evaluated on six publicly available kinship
databases and compared with best-reported results. It is observed that the
proposed SMNAE consistently yields best results on all the databasesComment: Accepted for publication in Transactions in Image Processin
Face Recognition: Too Bias, or Not Too Bias?
We reveal critical insights into problems of bias in state-of-the-art facial
recognition (FR) systems using a novel Balanced Faces In the Wild (BFW)
dataset: data balanced for gender and ethnic groups. We show variations in the
optimal scoring threshold for face-pairs across different subgroups. Thus, the
conventional approach of learning a global threshold for all pairs resulting in
performance gaps among subgroups. By learning subgroup-specific thresholds, we
not only mitigate problems in performance gaps but also show a notable boost in
the overall performance. Furthermore, we do a human evaluation to measure the
bias in humans, which supports the hypothesis that such a bias exists in human
perception. For the BFW database, source code, and more, visit
github.com/visionjo/facerec-bias-bfw.Comment: Conference on Computer Vision and Pattern Recognition (CVPR)
Workshops, 202
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
Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study
Deep learning-based methods have pushed the limits of the state-of-the-art in
face analysis. However, despite their success, these models have raised
concerns regarding their bias towards certain demographics. This bias is
inflicted both by limited diversity across demographics in the training set, as
well as the design of the algorithms. In this work, we investigate the
demographic bias of deep learning models in face recognition, age estimation,
gender recognition and kinship verification. To this end, we introduce the most
comprehensive, large-scale dataset of facial images and videos to date. It
consists of 40K still images and 44K sequences (14.5M video frames in total)
captured in unconstrained, real-world conditions from 1,045 subjects. The data
are manually annotated in terms of identity, exact age, gender and kinship. The
performance of state-of-the-art models is scrutinized and demographic bias is
exposed by conducting a series of experiments. Lastly, a method to debias
network embeddings is introduced and tested on the proposed benchmarks