Facial Kinship Verification: A Comprehensive Review and Outlook

Wu, Xiaoting; Feng, Xiaoyi; Cao, Xiaochun; Xu, Xin; Hu, Dewen; López, Miguel Bordallo; Liu, Li

doi:10.1007/s11263-022-01605-9

Cited by 17 publications

(14 citation statements)

References 163 publications

(170 reference statements)

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“…Then the metric learning-based methods [3], [37] were proposed to separate the kin and non-kin pairs. With the development of deep learning, many methods with different motivations were raised [1]. Among those, Li et al [38] proposed a Siamese network with the similarity metric to learn the discriminative features for kinship verification.…”

Section: Related Workmentioning

confidence: 99%

“…On the basis of image-based studies, to capture multi-source information, researchers proposed to study the kinship verification from facial videos. Compared with image-based studies [21], the works on facial videos [32]- [34] can only be found with a limited scale [1]. At the beginning, the constrained facial video dataset was used.…”

Section: Related Workmentioning

confidence: 99%

“…Facial Kinship Verification (FKV) aims at automatically determining whether two individuals have a kin relationship or not from their given facial images or videos [1]. Since the seminar work by Fang et.…”

Section: Introductionmentioning

confidence: 99%

“…Since the seminar work by Fang et. al [2] in 2010, recently FKV has gained increasing attention [1] due to its wide range of potential applications including finding missing persons, border control and customs, criminal investigations [3], family photo album organization, improving the performance of face recognition systems, and social media analysis [4]. To the best of our knowledge, although closely related to face verification, that has been well developed and made into products for real world [5], the FKV technology has not been, however, capable of performing at a sufficient level for any practical applications due to its unique challenges discussed in great detail in [1].…”

Section: Introductionmentioning

confidence: 99%

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Audio-Visual Kinship Verification: a New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach

Wu¹,

Feng²,

Zhang³

et al. 2022

Preprint

Self Cite

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<p>Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications, such as finding missing children, family photo organization, or criminal investigations. Over the past decade, many efforts have been devoted to improving the verification performance of human faces only while lacking other biometric information, e.g., speaking voice. In this paper, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the audio-visual kinship verification study. Since there is still no standard and public audiovisual kinship dataset, we first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep learning-based fusion method, named Unified Adaptive Adversarial Multimodal Learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multi-modal features. First, the modality adversarial learning eliminates the cross-modality variations by confusing the discriminator. The attention module quantifies the importance of kinship interested features. The overall multimodal fusion network is trained in Siamese fashion to encourage the compactness of kinship and separation of non-kinship. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Further, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human kinship verification ability on a sub-set of TALKIN-Family. It indicates that human has higher accuracy when they have access to both faces and voice. The machine learning methods could effectively and efficiently outperform human ability. Finally, we include the future work and research opportunities with the TALKIN-Family dataset.</p>

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Audio-Visual Kinship Verification: a New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach

Wu¹,

Feng²,

Zhang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…F ACIAL kinship verification (FKV) aims at automatically determining whether two individuals have a kin relationship or not from their given facial images or videos [1]. Since the seminar work by Fang et.…”

Section: Introductionmentioning

confidence: 99%

Audio-Visual Kinship Verification: A New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach

Zhang

López

et al. 2024

IEEE Trans. Cybern.

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Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications. Over the past decade, many efforts have been devoted to improving the verification performance from human faces only while lacking other biometric information, for example, speaking voice. In this article, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the audio-visual kinship verification study. We first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep-learning-based fusion method, called unified adaptive adversarial multimodal learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multimodal features. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Furthermore, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human verification ability on a

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Unveiling facial kinship: The BioKinVis dataset for facial kinship verification and genetic association studies

Yu,

Jin,

et al. 2023

Electrophoresis

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Facial image–based kinship verification represents a burgeoning frontier within the realms of computer vision and biomedicine. Recent genome‐wide association studies have underscored the heritability of human facial morphology, revealing its predictability based on genetic information. These revelations form a robust foundation for advancing facial image–based kinship verification. Despite strides in computer vision, there remains a discernible gap between the biomedical and computer vision domains. Notably, the absence of family photo datasets established through biological paternity testing methods poses a significant challenge. This study addresses this gap by introducing the biological kinship visualization dataset, encompassing 5773 individuals from 2412 families with biologically confirmed kinship. Our analysis delves into the distribution and influencing factors of facial similarity among parent–child pairs, probing the potential association between forensic short tandem repeat polymorphisms and facial similarity. Additionally, we have developed a machine learning model for facial image–based kinship verification, achieving an accuracy of 0.80 in the dataset. To facilitate further exploration, we have established an online tool and database, accessible at http://120.55.161.230:88/.

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Facial Kinship Verification: A Comprehensive Review and Outlook

Cited by 17 publications

References 163 publications

Audio-Visual Kinship Verification: a New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach

Audio-Visual Kinship Verification: a New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach

Audio-Visual Kinship Verification: A New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach

Unveiling facial kinship: The BioKinVis dataset for facial kinship verification and genetic association studies

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