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dc.contributor.authorLi, B.
dc.contributor.authorMian, A.
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorKrishna, Aneesh
dc.identifier.citationLi, B. and Mian, A. and Liu, W. and Krishna, A. 2015. Face recognition based on Kinect. Pattern Analysis and Applications. 19 (4): pp. 977-987.

In this paper, we present a new algorithm that utilizes low-quality red, green, blue and depth (RGB-D) data from the Kinect sensor for face recognition under challenging conditions. This algorithm extracts multiple features and fuses them at the feature level. A Finer Feature Fusion technique is developed that removes redundant information and retains only the meaningful features for possible maximum class separability. We also introduce a new 3D face database acquired with the Kinect sensor which has released to the research community. This database contains over 5,000 facial images (RGB-D) of 52 individuals under varying pose, expression, illumination and occlusions. Under the first three variations and using only the noisy depth data, the proposed algorithm can achieve 72.5 % recognition rate which is significantly higher than the 41.9 % achieved by the baseline LDA method. Combined with the texture information, 91.3 % recognition rate has achieved under illumination, pose and expression variations. These results suggest the feasibility of low-cost 3D sensors for real-time face recognition.

dc.publisherSpringer-Verlag London Ltd
dc.titleFace recognition based on Kinect
dc.typeJournal Article
dcterms.source.titlePattern Analysis and Applications
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available

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