Audio-Visual Kinship Verification: A New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach

Abstract

Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications. Over the past decade, many efforts have been devoted to improving the verification performance from human faces only while lacking other biometric information, for example, speaking voice. In this article, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the audio-visual kinship verification study. We first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep-learning-based fusion method, called unified adaptive adversarial multimodal learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multimodal features. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Furthermore, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human verification ability on a

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].…”