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A Unified Approach to Kinship Verification

In this work, we propose a deep learning-based approach for kin verification using a unified multi-task learning scheme where all kinship classes are jointly learned. This allows us to better utilize small training sets that are typical of kin verification. We introduce a novel approach for fusing the embeddings of kin images, to avoid overfitting, which is a common issue in training such networks. An adaptive sampling scheme is derived for the training set images, to resolve the inherent imbalance in kin verification datasets. A thorough ablation study exemplifies the effectivity of our approach, which is experimentally shown to outperform contemporary state-of-the-art kin verification results when applied to the Families In the Wild, FG2018, and FG2020 datasets.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.8 August 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2851 - 2857

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CONVOLUTIONAL NEURAL NETWORKS
MULTI-TASK LEARNING
FACE RECOGNITION
KINSHIP VERIFICATION
FACE BIOMETRICS

Info Detail Spesifik

DOI: 10.1109/TPAMI.2020.3036993

Pernyataan Tanggungjawab

Eran Dahan, Yosi Keller

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