title: Local structure-based image decomposition for feature extraction with applications to face recognition.
abstract: This paper presents a robust but simple image feature extraction method, called image decomposition based on local structure (IDLS). It is assumed that in the local window of an image, the macro-pixel (patch) of the central pixel, and those of its neighbors, are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images (also called structure images) according to a local structure feature vector. All the structure images, after being down-sampled for dimensionality reduction, are concatenated into one super-vector. Fisher linear discriminant analysis is then used to provide a low-dimensional, compact, and discriminative representation for each super-vector. The proposed method is applied to face recognition and examined using our real-world face image database, NUST-RWFR, and five popular, publicly available, benchmark face image databases (AR, Extended Yale B, PIE, FERET, and LFW). Experimental results show the performance advantages of IDLS over state-of-the-art algorithms.
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. qjjtx@126.com
authors.forename: Jianjun
authors.initials: J
authors.lastname: Qian
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.forename: Yong
authors.initials: Y
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D005149:Facial Expression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D012044:Regression Analysis