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Reference TypeConference Paper (Conference Proceedings)
Last Update2013:
Metadata Last Update2020: administrator
Citation KeyMouraGomeCarv:2013:ImFaVe
TitleAn Improved Face Verification Approach based on Speedup Robust Features and Pairwise Matching
DateAug. 5-8, 2013
Access Date2021, Jan. 19
Number of Files1
Size442 KiB
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Author1 Moura, Eduardo Santiago
2 Gomes, Herman Martins
3 de Carvalho, Joćo Marques
Affiliation1 Federal University of Campina Grande
2 Federal University of Campina Grande
3 Federal University of Campina Grande
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2013-07-13 02:03:16 :: -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsspeedup robust features, pairwise matching, face verification, Labeled Faces in the Wild, unsupervised protocol.
AbstractHuman faces are known to present large variability due to factors like pose and facial expression variations, changes in illumination and occlusion, among others, thus making face verification a very challenging problem. In this paper we address the problem of face verification with special interest on how to reduce degradation usually associated with face images acquired under uncontrolled environments. The approach we propose in this paper starts with a preprocessing step to correct in-plane face orientation and to compensate for illumination changes. SURF features are then extracted, which adds to the method a certain degree of invariance to pose, facial expression and other sources of variation. Taking the SURF features as input, an original pairwise face matching procedure is performed. The resulting matching scores are stored in a similarity matrix, which is then evaluated. An experimental study has revealed that the proposed approach produced the best ROC curve when compared to published work regarding the unsupervised setup of the Labeled Faces in the Wild (LFW) face database.
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