.png){kind=icon}
.png){kind=icon}
Return
A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2015.09.035.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
3D face recognition with the availability of only partial data (missing parts, occlusions and data corruptions) and single training sample is a highly challenging task. This paper presents an efficient 3D face recognition approach to address this challenge. We represent a facial scan with a set of local Keypoint-based Multiple Triangle Statistics (KMTS), which is robust to partial facial data, large facial expressions and pose variations. To address the single sample problem, we then propose a Two-Phase Weighted Collaborative Representation Classification (TPWCRC) framework. A class-based probability estimation is first calculated based on the extracted local descriptors as a prior knowledge. The resulting class-based probability estimation is then incorporated into the proposed classification framework as a locality constraint to further enhance its discriminating power. Experimental results on six challenging 3D facial datasets show that the proposed KMTS-TPWCRC framework achieves promising results for human face recognition with missing parts, occlusions, data corruptions, expressions and pose variations. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
3D face recognition
3D representation
Sparse representation
Partial facial data
Single sample problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
View more organizations