Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28885
Title: High-resolution face verification using pore-scale facial features
Authors: Li, D
Zhou, H
Lam, KM 
Keywords: Alignmenterror- robust
Expression invariance
Face recognition
Face verification
Pore-scale facial feature
Pose invariance
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2015, v. 24, no. 8, 7059198, p. 2317-2327 How to cite?
Journal: IEEE transactions on image processing 
Abstract: 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.
URI: http://hdl.handle.net/10397/28885
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2015.2412374
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