Kinship Verification Based on Cross-Generation Feature Interaction Learning

Kinship verification from facial images has been recognized as an emerging yet challenging technique in many potential computer vision applications. In this paper, we propose a novel cross-generation feature interaction learning (CFIL) framework for robust kinship verification. Particularly, an effe...

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Bibliographic Details
Published in:IEEE transactions on image processing 2021-01, Vol.30, p.1-1
Main Authors: Dong, Guan-Nan, Pun, Chi-Man, Zhang, Zheng
Format: Article
Language:English
Subjects:
Children
Computer vision
Deep learning
Distance measurement
face verification
Faces
Feature extraction
Kinship verification
Learning
Measurement
metric learning
Object recognition
Parents
Semantics
Similarity
Support vector machines
Verification
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Summary:Kinship verification from facial images has been recognized as an emerging yet challenging technique in many potential computer vision applications. In this paper, we propose a novel cross-generation feature interaction learning (CFIL) framework for robust kinship verification. Particularly, an effective collaborative weighting strategy is constructed to explore the characteristics of cross-generation relations by corporately extracting features of both parents and children image pairs. Specifically, we take parents and children as a whole to extract the expressive local and non-local features. Different from the traditional works measuring similarity by distance, we interpolate the similarity calculations as the interior auxiliary weights into the deep CNN architecture to learn the whole and natural features. These similarity weights not only involve corresponding single points but also excavate the multiple relationships cross points, where local and non-local features are calculated by using these two kinds of distance measurements. Importantly, instead of separately conducting similarity computation and feature extraction, we integrate similarity learning and feature extraction into one unified learning process. The integrated representations deduced from local and non-local features can comprehensively express the informative semantics embedded in images and preserve abundant correlation knowledge from image pairs. Extensive experiments demonstrate the efficiency and superiority of the proposed model compared to some state-of-the-art kinship verification methods.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2021.3104192