Show simple item record

dc.contributor.authorAlrjebi, M.
dc.contributor.authorPathirage, N.
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorLi, L.
dc.identifier.citationAlrjebi, M. and Pathirage, N. and Liu, W. and Li, L. 2017. Face recognition against occlusions via colour fusion using 2D-MCF model and SRC. Pattern Recognition Letters. 95: pp. 1339-1351.

In this paper, we propose a new method to tackle the problem of face recognition with occlusions via effectively using colour information. As most of current existing approaches for this problem focus on occlusion detection first as a pre-processing step and then solve recognition problem via occlusion removal, we will develop an effective image representation technique using fused colour information to increase recognition accuracy without occlusion detection. For such purpose, we would first devise a novel local representation for face images because occlusion often appears locally. Technically, we use the recently proposed two dimensional multi-colour fusion (2D-MCF) model in our previous work for image representation, as such model has been proven to be able to achieve very high performance in terms of recognition accuracy. Then, we use the Partitioned sparse sensing recognition (P-SRC) approach for face recognition since this approach is robust for occlusions and does not need any information about the occluded areas as required in most of the existing works. Finally, extensive experiments conducted on four different databases show the superiority of the proposed method over several existing approaches including P-SRC, the Reconstruction based methods and other state of the art methods. The proposed method can improve the face recognition accuracy by up to 24.5%, 3.8%, 25% and 2.86% for the AR database, the Curtin database, the FRGC database and the Busphorus database, respectively.

dc.publisherElsevier BV, North-Holland
dc.titleFace recognition against occlusions via colour fusion using 2D-MCF model and SRC
dc.typeJournal Article
dcterms.source.titlePattern Recognition Letters
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record