Single view-based 3D face reconstruction robust to self-occlusion

Youn Joo Lee, Sung Joo Lee, Kang Ryoung Park, Jaeik Jo, Jaihie Kim

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


State-of-the-art 3D morphable model (3DMM) is used widely for 3D face reconstruction based on a single image. However, this method has a high computational cost, and hence, a simplified 3D morphable model (S3DMM) was proposed as an alternative. Unlike the original 3DMM, S3DMM uses only a sparse 3D facial shape, and therefore, it incurs a lower computational cost. However, this method is vulnerable to self-occlusion due to head rotation. Therefore, we propose a solution to the self-occlusion problem in S3DMM-based 3D face reconstruction. This research is novel compared with previous works, in the following three respects. First, self-occlusion of the input face is detected automatically by estimating the head pose using a cylindrical head model. Second, a 3D model fitting scheme is designed based on selected visible facial feature points, which facilitates 3D face reconstruction without any effect from self-occlusion. Third, the reconstruction performance is enhanced by using the estimated pose as the initial pose parameter during the 3D model fitting process. The experimental results showed that the self-occlusion detection had high accuracy and our proposed method delivered a noticeable improvement in the 3D face reconstruction performance compared with previous methods.

Original languageEnglish
Article number176
JournalEurasip Journal on Advances in Signal Processing
Issue number1
Publication statusPublished - 2012

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012–0005223).

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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