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.
|Title of host publication||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 Apr 1|
|Event||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan|
Duration: 2019 Feb 27 → 2019 Mar 2
|Name||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings|
|Conference||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019|
|Period||19/2/27 → 19/3/2|
Bibliographical noteFunding 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).
© 2019 IEEE.
- Facial landmarks
- 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