Kinship verification via Frequency Feature Decoupling and Fusion

Abstract

In this paper, we propose a new Frequency Feature Decoupling and Fusion Network (FDFN) method for robust kinship verification. Our approach begins with a multi-scale fusion module designed to acquire features with enhanced discriminative power, which are then decoupled into high-frequency and low-frequency components. High-frequency features focus on the local details of the face, while low-frequency features emphasize the overall structural information. Furthermore, we introduce a hybrid spatial attention module to refine the high-frequency features, allowing the model to concentrate on more important facial regions. At the same time, the hybrid channel attention module is employed to optimize the low-frequency features, enabling the model to pay attention to the more significant feature channels within the overall structure. Finally, a fusion module then combines the refined high and low-frequency features to produce the final image representation. Our method effectively resolves the conflict between local details and global structure, optimizing each aspect separately to obtain more discriminative facial features. Experimental results on the FIW and Kinface datasets demonstrate that our approach achieves superior performance compared to baseline methods, establishing a robust foundation for kinship verification tasks and advancing the state of fine-grained image analysis in computer vision.