title: Face recognition by exploring information jointly in space, scale and orientation.
abstract: Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multiorientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones.
authors.affiliation: Center for Biometrics and Security Research and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. zlei@cbsr.ia.ac.cn
authors.forename: Zhen
authors.initials: Z
authors.lastname: Lei
authors.forename: Shengcai
authors.initials: S
authors.lastname: Liao
authors.forename: Matti
authors.initials: M
authors.lastname: Pietikäinen
authors.forename: Stan Z
authors.initials: SZ
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D056667:Biometric Identification / Q000379:methods*; D016002:Discriminant Analysis; D005145:Face / Q000033:anatomy & histology; D005149:Facial Expression; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012372:ROC Curve