ChildPredictor : A Child Face Prediction Framework with Disentangled Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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  • Xuehui Wang
  • Qiong Yan
  • Wei Shen
  • Wei Liu
  • Chun-Kit Wong
  • Chiu-Sing Pang
  • Buhua Liu

Related Research Unit(s)


Original languageEnglish
Journal / PublicationIEEE Transactions on Multimedia
Online published5 Apr 2022
Publication statusOnline published - 5 Apr 2022


The appearances of children are inherited from their parents, which makes it feasible to predict them. Predicting realistic childrens faces may help settle many social problems, such as age-invariant face recognition, kinship verification, and missing child identification. It can be regarded as an image-to-image translation task. Existing approaches usually assume domain information in the image-to-image translation can be interpreted by style, i.e., the separation of image content and style. However, such separation is improper for the child face prediction, because the facial contours between children and parents are not the same. To address this issue, we propose a new disentangled learning strategy for childrens face prediction. We assume that childrens faces are determined by genetic factors (compact family features, e.g., face contour), external factors (facial attributes irrelevant to prediction, such as moustaches and glasses), and variety factors (individual properties for each child). On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors. In order to obtain accurate genetic factors and perform the mapping, we propose a ChildPredictor framework. It transfers human faces to genetic factors by encoders and back by generators. Then, it learns the relationship between the genetic factors of parents and children through a mapping function. To ensure the generated faces are realistic, we collect a large Family Face Database to train ChildPredictor and evaluate it on the FF-Database validation set. Experimental results demonstrate that ChildPredictor is superior to other well-known image-to-image translation methods in predicting realistic and diverse child faces. Implementation codes can be found at

Research Area(s)

  • Child Face Prediction, Disentangled Learning, Face recognition, Faces, Generative Adversarial Network, Generative adversarial networks, Genetics, Glass, Image-to-image Translation, Skin, Training