Fusion of global and local features for face verification

Author

Fang, Yuchun ; Tan, Tieniu ; Wang, Yunhong

Author_Institution

Inst. of Autom., Acad. Sinica, Beijing, China

Volume

2

fYear

2002

fDate

2002

Firstpage

382

Abstract

A personalized feature combination scheme is proposed for face verification. ANFIS (adaptive neuro-fuzzy inference system) and SVM (support vector machine) are adopted respectively to form specialized feature representation for each subject by fusing global and local features. Instead of the common way for different subjects, we realize a new representation that adapts to each individual. Such adaptability in feature selection is inspired by face recognition in the HVS (human visual system) and results in an improved recognition rate.

Keywords

face recognition; feature extraction; fuzzy logic; inference mechanisms; learning automata; multilayer perceptrons; principal component analysis; visual databases; ANFIS; SVM; adaptability; adaptive neuro-fuzzy inference system; face recognition; face verification; feature selection; features fusion; global features; local features; personalized feature combination scheme; recognition rate; specialized feature representation; support vector machine; Adaptive systems; Face recognition; Facial features; Feature extraction; Humans; Inference algorithms; Nose; Performance analysis; Principal component analysis; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2002. Proceedings. 16th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-1695-X

Type

conf

DOI

10.1109/ICPR.2002.1048319

Filename

1048319