Paper Info
Title
Heterogeneous face image matching using multi-scale features
Abstract
Heterogeneous Face Recognition (HFR) refers recognition of face images captured in different modalities, e.g. Visual (VIS), near infrared (NIR) and thermal infrared (TIR). Although heterogeneous face images of a given person differ by pixel values, the identity of the face should be classified as the same. This paper focuses on NIR-VIS HFR. Light Source Invariant Features (LSIFs) are derived to extract the invariant parts between two types of face images. The derived LSIFs rely only on the variation patterns of the skin parameters so that the effects generated from light source can be largely reduced. A common feature extraction method is designed to capture LSIFs based on a group of differential-based band-pass image filters, and we show that the scale for filters is critical. Our results in CASIA HFB database validate the effectiveness of the model and our recognition approach.
Year
DOI
Venue
2012
10.1109/ICB.2012.6199762
ICB
Keywords
Field
DocType
pixel values,image matching,face recognition,heterogeneous face recognition,skin parameter variation patterns,heterogeneous face image matching,feature extraction method,lsif,casia hfb database,feature extraction,light source invariant features,differential-based band-pass image filters,nir-vis hfr,multiscale features,skin,band-pass filters,hfr,face,boosting,band pass filters,databases
Facial recognition system,Computer vision,Three-dimensional face recognition,Pattern recognition,Band-pass filter,Computer science,Image matching,Feature extraction,Boosting (machine learning),Invariant (mathematics),Artificial intelligence,Pixel
Conference
ISBN
Citations 
PageRank 
978-1-4673-0397-2
15
0.58
References 
Authors
11
4
Name
Order
Citations
PageRank
Sifei Liu122717.54
Dong Yi2117343.66
Zhen Lei33613157.95
Stan Z. Li48951535.26