Paper Info
Title
A Discriminative Model for Age Invariant Face Recognition
Abstract
Aging variation poses a serious problem to automatic face recognition systems. Most of the face recognition studies that have addressed the aging problem are focused on age estimation or aging simulation. Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. In this paper, we propose a discriminative model to address face matching in the presence of age variation. In this framework, we first represent each face by designing a densely sampled local feature description scheme, in which scale invariant feature transform (SIFT) and multi-scale local binary patterns (MLBP) serve as the local descriptors. By densely sampling the two kinds of local descriptors from the entire facial image, sufficient discriminatory information, including the distribution of the edge direction in the face image (that is expected to be age invariant) can be extracted for further analysis. Since both SIFT-based local features and MLBP-based local features span a high-dimensional feature space, to avoid the overfitting problem, we develop an algorithm, called multi-feature discriminant analysis (MFDA) to process these two local feature spaces in a unified framework. The MFDA is an extension and improvement of the LDA using multiple features combined with two different random sampling methods in feature and sample space. By random sampling the training set as well as the feature space, multiple LDA-based classifiers are constructed and then combined to generate a robust decision via a fusion rule. Experimental results show that our approach outperforms a state-of-the-art commercial face recognition engine on two public domain face aging data sets: MORPH and FG-NET. We also compare the performance of the proposed discriminative model with a generative aging model. A fusion of discriminative and generative models further improves the face matching accuracy in the presence of aging.
Year
DOI
Venue
2011
10.1109/TIFS.2011.2156787
IEEE Transactions on Information Forensics and Security
Keywords
Field
DocType
automatic face recognition system,discriminative model,face recognition study,face image,sift-based local feature,age invariant face recognition,appropriate feature representation,local descriptors,feature space,state-of-the-art commercial face recognition,mlbp-based local feature,feature extraction,image fusion,discriminant analysis,edge detection,scale invariant feature transform,face recognition,local binary pattern,public domain,random sampling
Scale-invariant feature transform,Computer science,Local binary patterns,Artificial intelligence,Discriminative model,Computer vision,Facial recognition system,Feature vector,Pattern recognition,Feature extraction,Linear discriminant analysis,Machine learning,Generative model
Journal
Volume
Issue
ISSN
6
3
1556-6013
Citations 
PageRank 
References 
77
2.00
37
Authors
3
Name
Order
Citations
PageRank
ZhiFeng Li1107355.45
Unsang Park281536.32
Anil Jain3335073334.84