title: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor.
abstract: This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The n(th)-order LDP is proposed to encode the (n-1)(th) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.
authors.affiliation: School of Automation Science and Electrical Engineering,Beihang University, Beijing 100191, China. bczhang@buaa.edu.cn
authors.forename: Baochang
authors.initials: B
authors.lastname: Zhang
authors.forename: Yongsheng
authors.initials: Y
authors.lastname: Gao
authors.forename: Sanqiang
authors.initials: S
authors.lastname: Zhao
authors.forename: Jianzhuang
authors.initials: J
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D006801:Humans