ENVIRONMENT COUPLED METRICS LEARNING FOR UNCONSTRAINED FACE VERIFICATION
Cai, Xinyuan; Wang, Chunheng; Xiao, Baihua; Zhou, Ji; Chen, Xue; Wang Chunheng
2012
Conference Name2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012)
Source PublicationInternational Conference on Image Processing (ICIP)
Pages577-580
Conference Date2012
Conference PlaceOrland, Florida, U.S.A
AbstractMaking recognition more reliable under unconstrained environment
is one of the most important challenges for realworld
face recognition. In this paper, we propose a novel
approach for unconstrained face verification. First, we use a
spectral-clustering method based on Structural Similarity
index to estimate the captured environments of facial images.
Then for each pair of environments, we learn two coupled
metrics, such that facial images captured in different environments
can be transformed into a media subspace, and
high recognition performance can be achieved. The coupled
transformations are jointly determined by solving an optimization
problem in the multi-task learning framework.
Experimental results on the benchmark dataset (LFW) show
the effectiveness of the proposed method in face verification
across varying environments.
KeywordFace Verification Metric Learning Unconstrained
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/5139
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Corresponding AuthorWang Chunheng
Recommended Citation
GB/T 7714
Cai, Xinyuan,Wang, Chunheng,Xiao, Baihua,et al. ENVIRONMENT COUPLED METRICS LEARNING FOR UNCONSTRAINED FACE VERIFICATION[C],2012:577-580.
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