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

Nermeen Nader; Fatma El-Zahraa El-Gamal; Shaker El-Sappagh; Kyung Sup Kwak; Mohammed Elmogy

doi:10.7717/peerj-cs.735

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

PeerJ Comput Sci. 2021 Dec 6:7:e735. doi: 10.7717/peerj-cs.735. eCollection 2021.

Authors

Nermeen Nader¹, Fatma El-Zahraa El-Gamal¹, Shaker El-Sappagh^{2

3}, Kyung Sup Kwak⁴, Mohammed Elmogy¹

Affiliations

¹ Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
² Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt.
³ Faculty of Computer Science and Engineering, Galala University, Suez, Egypt.
⁴ Department of Information and Communication Engineering, Inha University, Incheon, South Korea.

Abstract

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.

Keywords: Benchmark datasets; Deep learning techniques; Handcrafted feature-based techniques; Kinship recognition; Kinship verification.

Grants and funding

This work was supported by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2020R1A2B5B02002478. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.