Real-Time Facial Feature Extraction Scheme Using Cascaded Networks

Hyeonwoo Kim, Hyungjoon Kim, Een Jun Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Facial landmarks such as eyes, nose, and mouth are the most prominent feature points on the face. So far, many works have been done for efficiently extracting such landmarks from facial images. Utilizing more feature points for landmark extraction usually requires more processing time, which has been an obstacle to real-time processing or video processing. On the contrary, utilizing a too small number of feature points cannot represent diverse landmark properties such as shape accurately. In this paper, we propose a deep learning based method for extracting popular 68 feature points for facial landmarks quickly and accurately. To do that, we first detect all the faces in the image by using a cascaded structure composed of relatively light Convolution Neural Networks(CNN). Then, we perform facial landmark extraction for each face, which reduces the processing time a lot. We performed several experiments to evaluate the performance of our method. We report some of the results.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538677896
DOIs
Publication statusPublished - 2019 Apr 1
Event2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan
Duration: 2019 Feb 272019 Mar 2

Publication series

Name2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings

Conference

Conference2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019
CountryJapan
CityKyoto
Period19/2/2719/3/2

Keywords

  • cascaded structure
  • face alignment
  • face detection
  • Facial landmarks
  • MTCNN
  • real-time extraction

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Kim, H., Kim, H., & Hwang, E. J. (2019). Real-Time Facial Feature Extraction Scheme Using Cascaded Networks. In 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings [8679316] (2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2019.8679316