Real-Time Smile Detection using Deep Learning

Chi Cuong Nguyen, Giang Son Tran, Thi Phuong Nghiem, Jean-Christophe Burie, Chi Mai Luong
Author affiliations

Authors

  • Chi Cuong Nguyen University of Science and Technology of Hanoi
  • Giang Son Tran University of Science and Technology of Hanoi
  • Thi Phuong Nghiem University of Science and Technology of Hanoi
  • Jean-Christophe Burie University of La Rochelle
  • Chi Mai Luong Institute of Information Technology

DOI:

https://doi.org/10.15625/1813-9663/35/2/13315

Keywords:

Deep Learning, Convolutional Neural Network, Real-Time Smile Detection

Abstract

Real-time smile detection from facial images is useful in many real world applications such as automatic photo capturing in mobile phone cameras or interactive distance learning. In this paper, we study different architectures of object detection deep networks for solving real-time smile detection problem. We then propose a combination of a lightweight convolutional neural network architecture (BKNet) with an efficient object detection framework (RetinaNet). The evaluation on the two datasets (GENKI-4K, UCF Selfie) with a mid-range hardware device (GTX TITAN Black) show that our proposed method helps in improving both accuracy and inference time of the original RetinaNet to reach real-time performance. In comparison with the state-of-the-art object detection framework (YOLO), our method has higher inference time, but still reaches real-time performance and obtains higher accuracy of smile detection on both experimented datasets.

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Published

03-06-2019

How to Cite

[1]
C. C. Nguyen, G. S. Tran, T. P. Nghiem, J.-C. Burie, and C. M. Luong, “Real-Time Smile Detection using Deep Learning”, JCC, vol. 35, no. 2, p. 135–145, Jun. 2019.

Issue

Section

Computer Science