Tri-Subject Kinship Verification: Understanding the Core of A Family

Qin, Xiaoqian; Chen, Songcan

doi:10.48550/arxiv.1501.02555

Cited by 3 publications

(10 citation statements)

References 21 publications

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“…Similar to person Re-ID, existing works mainly focus on learning better features [6,9] and better distance metrics [23] for pairwise classification [22]. Recently, Qin et al [28] proposed a bilinear model to handle so-called tri-subject kinship verification problems. Fang et al [8] proposed a sparse group lasso based feature selection method to determine whether a query person is from a specific family.…”

Section: Related Workmentioning

confidence: 99%

“…Fang et al [8] proposed a sparse group lasso based feature selection method to determine whether a query person is from a specific family. Unlike [8,28], our method targets at a more general and challenging problem which can be used to predict an arbitrary number of images with a fixed structure of family roles, such as father-son, father-mother-daughter, grandfather-father-songrandson, etc.…”

Section: Related Workmentioning

confidence: 99%

“…The main difference between the two datasets is that each pair of face images in KinFaceW-II comes from the same photo while the image pairs in KinFace-I come from different photos. We follow the same protocol as that in [23,6,28] and use a 5-fold cross validation with balanced positive and negative pairs on the default training/testing split. Results are listed in Table 4.…”

Section: Kinship Verification and Identificationmentioning

confidence: 99%

“…Next we use TSKinFace dataset [28] for three-view kinship verification (i.e. father, mother, offspring), which contains 513 FM-S and 502 FM-D groups.…”

Section: Kinship Verification and Identificationmentioning

confidence: 99%

“…father, mother, offspring), which contains 513 FM-S and 502 FM-D groups. Following [28], we carry out a 5-fold cross validation with balanced positive and negative samples , and list the results in Table 5. As we see, our method performs consistently better than [28].…”

Section: Kinship Verification and Identificationmentioning

confidence: 99%

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Group Membership Prediction

Zhang¹,

Chen²,

Saligrama³

2015

Preprint

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The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a familial relationship. In this context we propose a novel probability model and introduce latent view-specific and view-shared random variables to jointly account for the view-specific appearance and crossview similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Kinship Verification and Identificationmentioning

confidence: 99%

“…Next we use TSKinFace dataset [28] for three-view kinship verification (i.e. father, mother, offspring), which contains 513 FM-S and 502 FM-D groups.…”

Section: Kinship Verification and Identificationmentioning

confidence: 99%

Section: Kinship Verification and Identificationmentioning

confidence: 99%

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Group Membership Prediction

Zhang¹,

Chen²,

Saligrama³

2015

Preprint

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Audio-Visual Kinship Verification in the Wild

Granger

Kinnunen

et al. 2019

2019 International Conference on Biometrics (ICB)

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Visual kinship verification entails confirming whether or not two individuals in a given pair of images or videos share a hypothesized kin relation. As a generalized face verification task, visual kinship verification is particularly difficult with low-quality found Internet data. Due to uncontrolled variations in background, pose, facial expression, blur, illumination and occlusion, state-of-the-art methods fail to provide high level of recognition accuracy. As with many other visual recognition tasks, kinship verification may benefit from combining visual and audio signals. However, voice-based kinship verification has received very little prior attention. We hypothesize that the human voice contains kin-related cues that are complementary to visual cues. In this paper we address, for the first time, the use of audio-visual information from face and voice modalities to perform kinship verification. We first propose a new multi-modal kinship dataset, called TALking KINship (TALKIN), that contains several pairs of Internet-quality video sequences. Using TALKIN, we then study the utility of various kinship verification methods including traditional local feature based methods (e.g. LBP, LPQ, etc.) and statistical methods (e.g., GMM-UBM and i-vector), and more recent deep learning approaches (e.g., VGG, LSTM and ResNet 50). Then, early and late fusion methods are evaluated on the TALKIN dataset for the study of kinship verification with both face and voice modalities. Finally, we propose a deep Siamese fusion network with contrastive loss for multi-modal fusion of kinship relations. Extensive experiments on the TALKIN dataset indicate that by combining face and voice modalities, the proposed Siamese network can provide a significantly higher level of accuracy compared to baseline uni-modal and multi-modal fusion techniques. Our experiments show that we can obtain an EER of 40.1% by using the voice modality, an EER of 32.5% by using the face modality, and, by combining face and voice modalities with the deep Siamese fusion network, we can achieve an EER of 29.8%. Experimental results also indicate that audio (vocal) information is complementary (to facial information) and useful for kinship verification.

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Recognizing Families In the Wild (RFIW)

Robinson

Shao

Zhao

et al. 2017

Proceedings of the 2017 Workshop on Recognizing Families in the Wild

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Recognizing Families In the Wild (RFIW)-an annual large-scale, multi-track automatic kinship recognition challenge supporting visual kin-based problems on scales larger than before. Organized in conjunction with the 15 th IEEE International Conference on Automatic Face and Gesture Recognition (FG), 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.

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Tri-Subject Kinship Verification: Understanding the Core of A Family

Cited by 3 publications

References 21 publications

Group Membership Prediction

Group Membership Prediction

Audio-Visual Kinship Verification in the Wild

Recognizing Families In the Wild (RFIW)

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