Optimal linear combination of facial regions for improving identification performance.
This paper presents a novel 3-D multiregion face recognition algorithm that consists of new geometric summation invariant features and an optimal linear feature fusion method. A summation invariant, which captures local characteristics of a facial surface, is extracted from multiple subregions of a 3-D range image as the discriminative features. Similarity scores between two range images are calculated from the selected subregions. A novel fusion method that is based on a linear discriminant analysis is developed to maximize the verification rate by a weighted combination of these similarity scores. Experiments on the Face Recognition Grand Challenge V2.0 dataset show that this new algorithm improves the recognition performance significantly in the presence of facial expressions.
Wong
Kin-Chung
KC
Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706-1691, USA. kinchungwong@gmail.com
Lin
Wei-Yang
WY
Hu
Yu Hen
YH
Boston
Nigel
N
Zhang
Xueqin
X
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016014:Linear Models; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity