A literature survey on kinship verification through facial images

Qin, Xiaoqian; Liu, Dakun; Wang, Dong

doi:10.1016/j.neucom.2019.09.089

Cited by 33 publications

(7 citation statements)

References 43 publications

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“…Kinship verification KV Aims at finding out whether there is a kin (i.e. family) relationship or not between given persons by analysing their facial images 11 . It might additionally detect the relation degree, e.g.…”

Section: Face Verification Fvmentioning

confidence: 99%

The Landscape of Facial Processing Applications in the Context of the European AI Act and the Development of Trustworthy Systems

Hupont

Tolan

Güneş

2022

Preprint

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This work focuses on facial processing, which refers to artificial intelligence (AI) systems that take facial images or videos as input data and perform some AI-driven processing to obtain higher-level information (e.g. a person’s identity, emotions, demographic attributes) or newly generated imagery (e.g. with modified facial attributes). Facial processing tasks, such as face detection, face identification, facial expression recognition or facial attribute manipulation, are generally studied as separate research fields and without considering a particular scenario, context of use or intended purpose. This paper studies the field of facial processing in a holistic manner. It establishes the landscape of key computational tasks, applications and industrial players in the field in order to identify the 60 most relevant applications adopted for real-world usages. These applications are analysed in the context of the new European AI Act and the 7 requirements for Trustworthy AI defined by the European High Level Expert Group on AI. More particularly, we assess the risk level conveyed by each application according to the AI Act and reflect on current research, technical and societal challenges towards trustworthy facial processing systems.

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Section: Face Verification Fvmentioning

confidence: 99%

The Landscape of Facial Processing Applications in the Context of the European AI Act and the Development of Trustworthy Systems

Hupont

Tolan

Güneş

2022

Preprint

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“…Wang et al [ 16 ] have done a comprehensive survey in 2020 to systematically categorize and evaluate the methods that emerged for kinship verification over the last decade. Then they have focused on the overall difficulties and challenges in the practical applications from the perspective of pattern recognition and computer vision.…”

Section: Related Workmentioning

confidence: 99%

A Study of Deep Learning-Based Face Recognition Models for Sibling Identification

Goel

Mehmood

Ugail

2021

Sensors

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Accurate identification of siblings through face recognition is a challenging task. This is predominantly because of the high degree of similarities among the faces of siblings. In this study, we investigate the use of state-of-the-art deep learning face recognition models to evaluate their capacity for discrimination between sibling faces using various similarity indices. The specific models examined for this purpose are FaceNet, VGGFace, VGG16, and VGG19. For each pair of images provided, the embeddings have been calculated using the chosen deep learning model. Five standard similarity measures, namely, cosine similarity, Euclidean distance, structured similarity, Manhattan distance, and Minkowski distance, are used to classify images looking for their identity on the threshold defined for each of the similarity measures. The accuracy, precision, and misclassification rate of each model are calculated using standard confusion matrices. Four different experimental datasets for full-frontal-face, eyes, nose, and forehead of sibling pairs are constructed using publicly available HQf subset of the SiblingDB database. The experimental results show that the accuracy of the chosen deep learning models to distinguish siblings based on the full-frontal-face and cropped face areas vary based on the face area compared. It is observed that VGGFace is best while comparing the full-frontal-face and eyes—the accuracy of classification being with more than 95% in this case. However, its accuracy degrades significantly when the noses are compared, while FaceNet provides the best result for classification based on the nose. Similarly, VGG16 and VGG19 are not the best models for classification using the eyes, but these models provide favorable results when foreheads are compared.

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“…Kinship topic is expected to attract growing interest from many fields, such as computer vision, pattern recognition, and machine learning, because of its difficulty and extensive functional applications. Despite many good works and trials on kinship verification issues, there are still various promising research directions that could be suggested ( Qin, Liu & Wang, 2020 ).…”

Section: Future Directionsmentioning

confidence: 99%

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

Nader

El-Gamal

El–Sappagh

et al. 2021

PeerJ Computer Science

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Background and Objectives Kinship verification and recognition (KVR) is the machine’s ability to identify the genetic and blood relationship and its degree between humans’ facial images. The face is used because it is one of the most significant ways to recognize each other. Automatic KVR is an interesting area for investigation. It greatly affects real-world applications, such as searching for lost family members, forensics, and historical and genealogical studies. This paper presents a comprehensive survey that describes KVR applications and kinship types. It presents a literature review of current studies starting from handcrafted passing through shallow metric learning and ending with deep learning feature-based techniques. Furthermore, kinship mostly used datasets are discussed that in turn open the way for future directions for the research in this field. Also, the KVR limitations are discussed, such as insufficient illumination, noise, occlusion, and age variations problems. Finally, future research directions are presented, such as age and gender variation problems. Methods We applied a literature survey methodology to retrieve data from academic databases. An inclusion and exclusion criteria were set. Three stages were followed to select articles. Finally, the main KVR stages, along with the main methods in each stage, were presented. We believe that surveys can help researchers easily to detect areas that require more development and investigation. Results It was found that handcrafted, metric learning, and deep learning were widely utilized in kinship verification and recognition problem using facial images. Conclusions Despite the scientific efforts that aim to address this hot research topic, many future research areas require investigation, such as age and gender variation. In the end, the presented survey makes it easier for researchers to identify the new areas that require more investigation and research.

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A literature survey on kinship verification through facial images

Cited by 33 publications

References 43 publications

The Landscape of Facial Processing Applications in the Context of the European AI Act and the Development of Trustworthy Systems

The Landscape of Facial Processing Applications in the Context of the European AI Act and the Development of Trustworthy Systems

A Study of Deep Learning-Based Face Recognition Models for Sibling Identification

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

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