TY - GEN
T1 - Personalized 3D-aided 2D facial landmark localization
AU - Zeng, Zhihong
AU - Fang, Tianhong
AU - Shah, Shishir K.
AU - Kakadiaris, Ioannis A.
PY - 2011
Y1 - 2011
N2 - Facial landmark detection in images obtained under varying acquisition conditions is a challenging problem. In this paper, we present a personalized landmark localization method that leverages information available from 2D/3D gallery data. To realize a robust correspondence between gallery and probe key points, we present several innovative solutions, including: (i) a hierarchical DAISY descriptor that encodes larger contextual information, (ii) a Data-Driven Sample Consensus (DDSAC) algorithm that leverages the image information to reduce the number of required iterations for robust transform estimation, and (iii) a 2D/3D gallery pre-processing step to build personalized landmark metadata (i.e., local descriptors and a 3D landmark model). We validate our approach on the Multi-PIE and UHDB14 databases, and by comparing our results with those obtained using two existing methods.
AB - Facial landmark detection in images obtained under varying acquisition conditions is a challenging problem. In this paper, we present a personalized landmark localization method that leverages information available from 2D/3D gallery data. To realize a robust correspondence between gallery and probe key points, we present several innovative solutions, including: (i) a hierarchical DAISY descriptor that encodes larger contextual information, (ii) a Data-Driven Sample Consensus (DDSAC) algorithm that leverages the image information to reduce the number of required iterations for robust transform estimation, and (iii) a 2D/3D gallery pre-processing step to build personalized landmark metadata (i.e., local descriptors and a 3D landmark model). We validate our approach on the Multi-PIE and UHDB14 databases, and by comparing our results with those obtained using two existing methods.
UR - http://www.scopus.com/inward/record.url?scp=79952502259&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-19309-5_49
DO - 10.1007/978-3-642-19309-5_49
M3 - Conference contribution
AN - SCOPUS:79952502259
SN - 9783642193088
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 633
EP - 646
BT - Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
T2 - 10th Asian Conference on Computer Vision, ACCV 2010
Y2 - 8 November 2010 through 12 November 2010
ER -