Computer Graphics
TU Braunschweig

Automatic Infant Face Verification via Convolutional Neural Networks

In this paper, we investigate how convolutional neural networks (CNN) can learn to solve the verification task for faces of young children. One of the main issues of automatic face verification approaches is how to deal with facial changes resulting from aging. Since the facial shape and features change drastically during early childhood, the recognition of children can be challenging even for human observers. Therefore, we design CNNs that take two infant photographs as input and verify whether they belong to the same child. To specifically train our CNNs to recognize young children, we collect a new infant face dataset including 4,528 face images of 42 subjects in the age range of 0 to 6 years. Our results show an accuracy of up to 85 percent for face verification using our dataset with no overlapping subjects between the training and test data, and 72 percent in the FG-NET dataset for the age range from 0 to 4 years.

Author(s):Leslie Wöhler, Hangjian Zhang, Georgia Albuquerque, Marcus Magnor
Published:to appear
Type:Article in conference proceedings
Book:Proc. Vision, Modeling and Visualization (VMV)
Note:The two first authors contributed equally to this work.

  title = {Automatic Infant Face Verification via Convolutional Neural Networks},
  author = {W{\"o}hler, Leslie and Zhang, Hangjian  and Albuquerque, Georgia and Magnor, Marcus},
  booktitle = {Proc. Vision, Modeling and Visualization ({VMV})},
  organization = {Eurographics},
  note = {The two first authors contributed equally to this work.},
  year = {2018}