Paper Info
Title
Geometric histograms of 3D keypoints for face identification with missing parts
Abstract
In this work, an original solution to 3D face identification is proposed, which supports recognition also in the case of probes with missing parts. Distinguishing traits of the face are captured by first extracting 3D keypoints of a face scan, then measuring how the face surface changes in the keypoints neighborhood using a local descriptor. To this end, an adaptation of the meshDOG algorithm to the case of 3D faces is proposed, together with a multi-ring geometric histogram descriptor. Face similarity is then evaluated by comparing local keypoint descriptors across inlier pairs of matching keypoints between probe and gallery scans. Experiments have been performed to assess the keypoints distribution and repeatability. Recognition accuracy of the proposed approach has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face biometrics solutions.
Year
DOI
Venue
2013
10.2312/3DOR/3DOR13/057-064
3DOR
Keywords
Field
DocType
keypoints distribution,face similarity,recognition accuracy,missing part,multi-ring geometric histogram descriptor,keypoints neighborhood,local descriptor,local keypoint descriptors,face identification,face surface change,surface,solid,applications,curve
Computer vision,Histogram,Pattern recognition,Artificial intelligence,Biometrics,Mathematics
Conference
Citations 
PageRank 
References 
4
0.38
17
Authors
4
Name
Order
Citations
PageRank
Stefano Berretti188052.33
Naoufel Werghi232641.99
Alberto Del Bimbo33777420.44
Pietro Pala4123991.64