Abstract

In this paper, we investigated dual VGG-Face convolutional neural network classifier for image-based kinship verification problems. The proposed classifier is formed by paralleling convolutional layers of VGG CNN architecture and combined it with several fully-connected layers. Although the VGG CNN architecture is consists of huge parameters, the number of parameters in the classifier will be reduced by more than 80% by removing the fully-connected layers of the original classifier. We use the multi-task loss function for the training process to ensure that the features learned by the classifier are also can be used for family classification. Experiments on the FIW kinship verification dataset show that dual VGG-Face CNN classifiers can achieve an average accuracy of 64.71% on a single classifier and 65.49% on ensemble configuration. Based on our experiments, the lowest accuracy is produced for second-generation kinship (grandchild-grandparent) which have a low number of examples on FIW kinship.

Original languageEnglish
Title of host publicationIoTaIS 2020 - Proceedings
Subtitle of host publication2020 IEEE International Conference on Internet of Things and Intelligence Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages123-128
Number of pages6
ISBN (Electronic)9781728194486
DOIs
Publication statusPublished - 27 Jan 2021
Event2020 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2020 - Virtual, Bali, Indonesia
Duration: 27 Jan 202128 Jan 2021

Publication series

NameIoTaIS 2020 - Proceedings: 2020 IEEE International Conference on Internet of Things and Intelligence Systems

Conference

Conference2020 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2020
Country/TerritoryIndonesia
CityVirtual, Bali
Period27/01/2128/01/21

Keywords

  • convolutional neural network
  • dual VGG-Face
  • image-based kinship verification

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