A review on kinship verification from facial information.

Kinship verification is a challenging computer vision task that aims to mainly answer the question: “Are these two persons blood relatives?”. It is an important area of research with many applications, including the identification of criminals or wanted suspects through their relatives, genealogical studies, face recognition improvements, scrapbook organization, character selection for movies, the search for missing family members, and social media analysis. Despite the significant advancements to date, automatic kinship verification remains challenging to accomplish. Moreover, the datasets used to evaluate the developed kinship verification methods reveal intrinsic problems, including changes in the subject’s facial information caused by age variation, gender and ethnic differences, as well as external difficulties that are mainly related to the acquisition parameters, such as the various imaging settings and the uncooperative subjects (datasets collected from different sources). Recently, due to the great success of the deep learning models for image and video classification, kinship verification methods have seen a significant shift from the classical metric learning and machine learning methods toward both deep learning and also hybrid methods (ensembles of different methods). Therefore, this paper presents an overview of kinship verification using facial information methods as well as the available image and video datasets used to evaluate them. Besides, an experimental study is carried out to highlight the most efficient methods. Finally, a discussion is provided to weigh up the strengths and limitations of the recent kinship verification approaches and suggest future research directions.Graphic abstract: Kinship verification is a challenging computer vision task that aims to mainly answer the question: “Are these two persons blood relatives?”. It is an important area of research with many applications, including the identification of criminals or wanted suspects through their relatives, genealogical studies, face recognition improvements, scrapbook organization, character selection for movies, the search for missing family members, and social media analysis. Despite the significant advancements to date, automatic kinship verification remains challenging to accomplish. Moreover, the datasets used to evaluate the developed kinship verification methods reveal intrinsic problems, including changes in the subject’s facial information caused by age variation, gender and ethnic differences, as well as external difficulties that are mainly related to the acquisition parameters, such as the various imaging settings and the uncooperative subjects (datasets collected from different sources). Recently, due to the great success of the deep learning models for image and video classification, kinship verification methods have seen a significant shift from the classical metric learning and machine learning methods toward both deep learning and also hybrid methods (ensembles of different methods). Therefore, this paper presents an overview of kinship verification using facial information methods as well as the available image and video datasets used to evaluate them. Besides, an experimental study is carried out to highlight the most efficient methods. Finally, a discussion is provided to weigh up the strengths and limitations of the recent kinship verification approaches and suggest future research directions. [ABSTRACT FROM AUTHOR]