Posed face image synthesis using nonlinear manifold learning

Eunok Cho, Daijin Kim, Sang Youn Lee

Research output: Contribution to journalArticle

2 Citations (Scopus)


This paper proposes to synthesize posed facial images from two parameters for the pose. This parameterization makes the representation, storage, and transmission of face images effective. Because variations of face images show a complicated nonlinear manifold in high-dimensional data space, we use an LLE (Locally Linear Embedding) technique for a good representation of face images. And we apply a snake model to estimate face feature values in the reduced feature space that corresponds to a specific pose parameter. Finally, a synthetic face image is obtained from an interpolation of several neighboring face images. Experimental results show that the proposed method creates an accurate and consistent synthetic face images with respect to changes of pose.

Original languageEnglish
Pages (from-to)946-954
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2003 Dec 1

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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