Real-Time Facial Feature Extraction Scheme Using Cascaded Networks

Hyeonwoo Kim, Hyungjoon Kim, Eenjun Hwang

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

    14 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
    Country/TerritoryJapan
    CityKyoto
    Period19/2/2719/3/2

    Bibliographical note

    Funding Information:
    ACKNOWLEDGMENT This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. R0190- 16-2012, High Performance Big Data Analytics Platform Performance Acceleration Technologies Development) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09919590).

    Publisher Copyright:
    © 2019 IEEE.

    Keywords

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

    ASJC Scopus subject areas

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

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