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.

Nowadays, kinship verification is considered an attractive research area with a great interest in computer vision. It significantly affects applications in the real world, such as finding missing individuals, forensics, and genealogical research. However, verifying kinship relations between people using facial images is not straightforward. Many limitations affect kinship verification accuracy. Therefore, this paper proposes a new approach for verifying kinship based on facial image analysis. The proposed approach goes into six stages: preprocessing, feature extraction, feature normalization, feature fusion, feature representation, and kinship verification. The preprocessing stage is responsible for converting RGB images into other color models. Different types of handcrafted feature descriptors (i.e., color and texture descriptors) are extracted in the feature extraction stage. The texture features are represented by scale invariant feature transform (SIFT), local binary pattern (LBP), and heterogeneous auto-similarities of characteristics (HASC), whereas the color features are represented by color correlogram (CC) and dense color histogram (DCH). Then, all the features are set to the same range in the feature normalization stage to be suitable for feature fusion. The feature fusion stage takes place where the different types of features are concatenated together. Next, in the feature representation stage, the parent and child features are gathered into one feature vector. Finally, the kinship verification stage produces the final decision of being kin or non-kin using the gentle AdaBoost ensemble classifier. KinFaceW-I and KinFaceW-II datasets were used to evaluate the proposed approach, where the obtained results were 79.54\% and 90.65\%, respectively. It is noteworthy that the proposed approach outperforms many state-of-the-art approaches that verify kinship, including those dependent on metric learning and deep convolutional neural nets (CNNs).

Nowadays, kinship verification is an attractive research area within computer vision. It significantly affects applications in the real world, such as finding missing individuals and forensics. Despite the importance of this research topic, it still faces many challenges, such as low accuracy and illumination variations. Due to the existence of different classes of feature extraction techniques, different types of information can be extracted from the input data. Moreover, the fusion power produces complementary information that can address kinship verification problems. Therefore, this paper proposes a new approach for verifying kinship by fusing features from different perspectives, including color-texture and color features in different color spaces. Besides using promising methods in the field, such as local binary pattern (LBP) and scale-invariant feature transform (SIFT), the paper utilizes other feature extraction methods, which are heterogeneous auto-similarities of characteristics (HASC), color correlogram (CC), and dense color histogram (DCH). As far as we know, these features haven’t been employed before in this research area. Accordingly, the proposed approach goes into six stages: preprocessing, feature extraction, feature normalization, feature fusion, feature representation, and kinship verification. The proposed approach was evaluated on the KinFaceW-I and KinFaceW-II field standard datasets, achieving maximum accuracy of 79.54% and 90.65%, respectively. Compared with many state-of-the-art approaches, the results of the proposed approach reflect the promising achievements and encourage the authors to plan for future enhancement.

No abstract