title: Local directional number pattern for face analysis: face and expression recognition.
abstract: This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts directional information, and we encode such information using the prominent direction indices (directional numbers) and sign-which allows us to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, and extract the distribution of the LDN features from them. Then, we concatenate these features into a feature vector, and we use it as a face descriptor. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression, and time lapse variations. Moreover, we test our descriptor with different masks to analyze its performance in different face analysis tasks.
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. adin@khu.ac.kr
authors.forename: Adin Ramirez
authors.initials: AR
authors.lastname: Rivera
authors.forename: Jorge Rojas
authors.initials: JR
authors.lastname: Castillo
authors.forename: Oksam
authors.initials: O
authors.lastname: Chae
mesh_terms: D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D005149:Facial Expression; D005260:Female; D006801:Humans; D008297:Male; D060388:Support Vector Machine