Facial Landmark Extraction Scheme Based on Semantic Segmentation

Hyung Joon Kim, Jisoo Park, Hyeon Woo Kim, Eenjun Hwang

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

    11 Citations (Scopus)

    Abstract

    Facial landmark is a set of features that can be distinguished in the human face with the naked eye. Typical facial landmark includes eyes, eyebrows, nose and mouth. It plays an important role in the human-related image analysis. For example, it can be used to determine whether human beings exist in the image, identify who the person is or recognize the orientation of a face when photographing. Methods for detecting facial landmark can be classified into two groups: One group is based on traditional image processing techniques such as Haar-cascade and edge detection. The other group is based on machine learning technique where landmark is detected through training facial features. However, such techniques have shown low accuracy, especially in the exceptional conditions such as low luminance or overlapped face. To overcome this problem, we propose a new facial landmark extraction scheme using deep learning and semantic segmentation and demonstrate that with even small dataset, our scheme can achieve excellent facial landmark extraction performance.

    Original languageEnglish
    Title of host publication2018 International Conference on Platform Technology and Service, PlatCon 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781538647103
    DOIs
    Publication statusPublished - 2018 Sept 25
    Event2018 International Conference on Platform Technology and Service, PlatCon 2018 - Jeju, Korea, Republic of
    Duration: 2018 Jan 292018 Jan 31

    Publication series

    Name2018 International Conference on Platform Technology and Service, PlatCon 2018

    Other

    Other2018 International Conference on Platform Technology and Service, PlatCon 2018
    Country/TerritoryKorea, Republic of
    CityJeju
    Period18/1/2918/1/31

    Bibliographical note

    Publisher Copyright:
    © 2018 IEEE.

    Keywords

    • Convolutional Neural Network
    • Facial Landmark
    • Feature Extraction
    • SegNet
    • Semantic Segmentation
    • VGGNet

    ASJC Scopus subject areas

    • Safety, Risk, Reliability and Quality
    • Hardware and Architecture
    • Computer Networks and Communications
    • Computer Science Applications

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