Kinship Verification Method of Face Image Deep Feature Fusion

Abstract

Kinship verification is an important and challenging problem in computer vision. How to extract discriminative features is the key to improve the accuracy of kinship verification. At present, convolutional neural networks (CNNs) for feature extraction in the field of computer vision has achieved remarkable success, making it the most scholars used to study kinship verification related issues. However, few people use the self-attention mechanism with global capture capability to build a backbone feature classification network. Therefore, this paper proposes a backbone feature extraction network model based on a non-convolution, which expands the selection range of traditional classification networks for kinship verification related issues. Specifically, the paper proposes to use Vision Transformers as the basic backbone feature extraction network, combined with CNN with local attention mechanism, to provide a unique integrated solution in kinship verification. The proposed GLANet model is used for kinship verification and can verify 11 kinship pairs. The final experimental results show that in the FIW dataset, compared with the RFIW2020 challenge leading method, the proposed method has better verification effect in kinship, and the accuracy rate can reach 79.6 %.