Photo to Family Tree: Deep Kinship Understanding for Nuclear Family Photos
MM '18: ACM Multimedia Conference Seoul Republic of Korea October, 2018(2018)
摘要
Multi-person kinship recognition is complicated and challenging as an extension of existing studies on recognizing kinship in pairwise face images independently, with little existing literature. In this paper, we extend kinship recognition from two persons to a nuclear family consisting of multiple persons. To generate the corresponding family tree from one nuclear family photo automatically, we propose a novel Deep Kinship Recognition (DKR) framework. Firstly, we propose a deep kinship classification model (named DKC-KGA) which leverages kin-or-not, gender and relative age attributes to predict kinship categories. Then, based on the outputs of DKC-KGA for an input nuclear family photo, we develop a reasoning conditional random field (R-CRF) model to infer the optimal corresponding family tree by utilizing the common knowledge w.r.t. kinship of a nuclear family. Our DKR framework gains superior performance on both Group-Face dataset and TSKinFace dataset, compared with state-of-the-arts.
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关键词
Kinship Recognition, Deep Learning, R-CRF Algorithm
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