ItemConference Paper

Face-Based Kinship Verification using Deep Embeddings for Low-Cost Health Record Linkage

Title
Face-Based Kinship Verification using Deep Embeddings for Low-Cost Health Record Linkage
Creator
Balamurugan, Rahul; Sekar, Kency Taniya Antony; Nandalal, V.; Wu, Congyu Peter
Description
Precise linkage of health records is essential for continuity of care, reducing duplicate health records, and accurately documenting family medical histories. Genomic testing offers the evidence-based biological 'gold standard' for verifying kinship; however, access to testing is either impossible or unavailable in most low-resourced environments due to prohibitive costs, long timelines, and/or lack of infrastructure. This study provides a low cost and interpretable pipeline for kinship verification in the form of Siamese deep embeddings. The processed facial image embeddings produced by a ResNet-18 backbone using 256-dimensional and L2-normalized embeddings, are then compared using cosine similarity. A validation-based calibration process selects the logit polarity and decision threshold that support stable deployment decisions. Grad-CAM visualizations can be interpreted frame-by-frame and allow for pair-specific attributions of faces that were more relevant or important in decisions of similarity. In experiments on the Families in the Wild (FIW) dataset (family-disjoint splits), we report ROC-AUC of 0.834, target balanced accuracy of ?0.88, with similar precision, recall, and specificity. The confusion matrices also illustrate a near symmetric distribution of errors by family and both Grad-CAM explanations highlight how the model came to a decision for true cases and hard cases. The above results illustrate how we can deploy a lightweight, explainable, and face-based kinship verification pipeline on a CPU-only system. Our study therefore provides a feasible assistive tool for health record linkage where genomic validation is not possible. 2025 IEEE.
Source
2025 IEEE 1st International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability, ICINVENTS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
deep learning; face embeddings; health record linkage; healthcare informatics; Kinship verification
Coverage
Balamurugan R., School of Systems Science and Industrial Engineering, Binghamton University, New York, United States, School of Science, CHRIST, Deemed to be University, Pune, Lavasa, India; Sekar K.T.A., School of Systems Science and Industrial Engineering, Binghamton University, New York, United States; Nandalal V., Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India; Wu C.P., School of Systems Science and Industrial Engineering, Binghamton University, New York, United States
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155662-4;
Format
online
Language
English
Type
Conference paper

Citation

Balamurugan, Rahul; Sekar, Kency Taniya Antony; Nandalal, V.; Wu, Congyu Peter, “Face-Based Kinship Verification using Deep Embeddings for Low-Cost Health Record Linkage,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26040.

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