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Neural Computing and Applications

, Volume 31, Issue 12, pp 8583–8592 | Cite as

A hybrid framework for smile detection in class imbalance scenarios

  • Thanh Vo
  • Trang Nguyen
  • C. T. LeEmail author
Original Article

Abstract

In this study, we consider the problem of smile detection in both an imbalanced data scenario, in which the number of smile images is in the minority compared with the number of neutral images, and a balanced data scenario. We first propose a smile detection model using a convolutional neural network (SD-CNN) to improve the performance in the balanced data scenario, and then a hybrid deep learning framework (HF-SD) that uses a modification of the SD-CNN model to learn and then extracts the features from dataset. These extracted features are then used to train an extreme gradient boosting approach to handle the imbalanced problem. An experiment shows that the proposed model has impressive discriminative ability for smile detection, in both balanced and imbalanced data scenarios, compared with existing approaches. HF-SD yields an accuracy of 93.6% and outperforms the state-of-the-art approaches for the original GENK14K database in the balanced data scenario. The results of the second experiment show that HF-SD also achieves better AUCs (area under the receiver operating characteristic curve) compared with the state-of-the-art methods for smile detection in an imbalanced data scenario with different balancing ratios.

Keywords

Smile detection Deep learning Imbalanced scenarios Machine vision 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  2. 2.Faculty of Information TechnologyHo Chi Minh City Open UniversityHo Chi Minh CityVietnam
  3. 3.Faculty of Information TechnologyHo Chi Minh City University of Technology (HUTECH)Ho Chi Minh CityVietnam

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