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

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.

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

“…FKV involves automatically determining whether two individuals share a kin relationship based on their facial images. This area of study has generated increasing attention, particularly within computer vision field [9,[19][20][21][22][23][24]. FKV holds significant potential for applications in public social security, such as locating missing persons and aiding criminal investigations [25].…”

“…However, a major limitation is the use of picture datasets collected mainly from the web, featuring celebrities, sports figures, and politicians. In the case of kinship, the family datasets include wild, Cornell KinFace, UB KinFace, KinFaceW-I, KinFaceW-II, Tri-Subject Kinship Face (TSKinFace), and Family 101 [9,10]. Establishing a dataset through biological paternity testing methods would significantly enhance this research, offering crucial data to understand the distribution and influencing factors of facial similarity among parent-child pairs.…”

Enhanced kinship verification analysis based on color and texture handcrafted techniques

“…Then, to develop a facial kinship verification system, the faces must be effectively represented using features. Therefore, the techniques that attempted to extract discriminating features could be generally divided into three categories 7,19 : handcrafted feature-based, shallow metric feature-based, and deep learning feature-based techniques. The following subsections present the previous studies' methods and approaches used for kinship verification based on these feature extraction categories and utilizing KFW-I and KFW-II for their studies' evaluation.…”

“…Kinship verification is an exciting area of research that is useful for many critical applications, such as identifying family members from their images, forensics, genealogical research, and missing children finding [7][8][9] . Furthermore, kinship verification is additional assistance to complicated and expensive Deoxyribonucleic acid (DNA) verification 10 .…”