Release Time:2019-03-11  Hits:

Indexed by: Conference Paper

Date of Publication: 2015-06-29

Included Journals: Scopus、SCIE、CPCI-S、EI

Volume: 2015-August

Key Words: Facial feature extraction; projective invariant; characteristic number; regression

Abstract: Facial feature extraction plays an important role in many multimedia and vision applications. Recent regression methods for extraction lack the explicit shape constraints for faces, and require a large number of facial images covering great appearance variations. This paper introduces a novel projective invariant, named characteristic number (CN), to explicitly characterize the intrinsic geometries of facial points shared by human faces, which is inherently invariant to pose changes. By further developing a shape-to-gradient regression framework, we provide a robust and efficient feature extractor for facial images in the wild. The computation of our model can be successfully addressed by learning the descent directions using point-CN pairs without the need of large collections for appearance training. Extensive experiments on challenging benchmark data sets demonstrate the effectiveness of our proposed detector against other state-of-the-art approaches.