Title :
Occluded face recognition based on the improved SVM and block weighted LBP
Author :
Chen, Zhaohua ; Xu, Tingrong ; Han, Zhiyuan
Author_Institution :
Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou, China
Abstract :
Facial occlusions, for example, sunglasses, and scarves, etc., can significantly affect the performance of any facial recognition system. The focus of this paper is on facial occlusions, and particularly, on how to improve the recognition of faces occluded by sunglasses and scarves. We propose a new approach that consists of first detecting the presence of sunglasses/scarves and then processing the non-occluded facial regions only. The occlusion detection problem is approached using PCA and improved support vector machines (SVM), while the recognition of the non-occluded facial part is performed using blocked-based weighted local binary patterns (LBP).
Keywords :
face recognition; hidden feature removal; object detection; principal component analysis; support vector machines; PCA; SVM; block weighted LBP; facial occlusion; local binary pattern; occluded face recognition; principal component analysis; scarve detection; sunglass detection; support vector machines; Databases; Face; Face recognition; Feature extraction; Principal component analysis; Support vector machines; Training; face recognition; local binary patterns; occlusion detection; support vector machines;
Conference_Titel :
Image Analysis and Signal Processing (IASP), 2011 International Conference on
Conference_Location :
Hubei
Print_ISBN :
978-1-61284-879-2
DOI :
10.1109/IASP.2011.6109010