TY - GEN
T1 - Facial Landmark Extraction Scheme Based on Semantic Segmentation
AU - Kim, Hyung Joon
AU - Park, Jisoo
AU - Kim, Hyeon Woo
AU - Hwang, Eenjun
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/25
Y1 - 2018/9/25
N2 - 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.
AB - 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.
KW - Convolutional Neural Network
KW - Facial Landmark
KW - Feature Extraction
KW - SegNet
KW - Semantic Segmentation
KW - VGGNet
UR - http://www.scopus.com/inward/record.url?scp=85052657183&partnerID=8YFLogxK
U2 - 10.1109/PlatCon.2018.8472730
DO - 10.1109/PlatCon.2018.8472730
M3 - Conference contribution
AN - SCOPUS:85052657183
T3 - 2018 International Conference on Platform Technology and Service, PlatCon 2018
BT - 2018 International Conference on Platform Technology and Service, PlatCon 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Platform Technology and Service, PlatCon 2018
Y2 - 29 January 2018 through 31 January 2018
ER -