High-resolution face verification using pore-scale facial features

Dong Li, Huiling Zhou, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)


Face recognition methods, which usually represent face images using holistic or local facial features, rely heavily on alignment. Their performances also suffer a severe degradation under variations in expressions or poses, especially when there is one gallery per subject only. With the easy access to high-resolution (HR) face images nowadays, some HR face databases have recently been developed. However, few studies have tackled the use of HR information for face recognition or verification. In this paper, we propose a pose-invariant face-verification method, which is robust to alignment errors, using the HR information based on pore-scale facial features. A new keypoint descriptor, namely, pore-Principal Component Analysis (PCA)-Scale Invariant Feature Transform (PPCASIFT) - adapted from PCA-SIFT - is devised for the extraction of a compact set of distinctive pore-scale facial features. Having matched the pore-scale features of two-face regions, an effective robust-fitting scheme is proposed for the face-verification task. Experiments show that, with one frontal-view gallery only per subject, our proposed method outperforms a number of standard verification methods, and can achieve excellent accuracy even the faces are under large variations in expression and pose.
Original languageEnglish
Article number7059198
Pages (from-to)2317-2327
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number8
Publication statusPublished - 1 Aug 2015


  • alignmenterror- robust
  • expression invariance
  • face recognition
  • face verification
  • pore-scale facial feature
  • pose invariance

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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