Fisher Vector Faces in the Wild

Karen Simonyan; Omkar Parkhi; Andrea Vedaldi; Andrew Zisserman

doi:10.5244/c.27.8

Abstract

Several recent papers on automatic face verification have significantly raised the performance bar by developing novel, specialised representations that outperform standard features such as SIFT for this problem. This paper makes two contributions: first, and somewhat surprisingly, we show that Fisher vectors on densely sampled SIFT features, i.e. an off-the-shelf object recognition representation, are capable of achieving state-of-the-art face verification performance on the challenging “Labeled Faces in the Wild” benchmark; second, since Fisher vectors are very high dimensional, we show that a compact descriptor can be learnt from them using discriminative metric learning. This compact descriptor has a better recognition accuracy and is very well suited to large scale identification tasks.

Keywords

Computer scienceVector (molecular biology)Artificial intelligenceBiologyGenetics

Affiliated Institutions

University of Oxford GB

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Publication Info

Year: 2013
Type: article
Pages: 8.1-8.11
Citations: 455
Access: Closed

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APA Style

                            
                                    Karen Simonyan, 
                                
                                    Omkar Parkhi, 
                                
                                    Andrea Vedaldi
                                
                                et al.
                            
                            (2013). 
                            Fisher Vector Faces in the Wild. 
                            
                            , 8.1-8.11.
                            https://doi.org/10.5244/c.27.8

Identifiers

DOI: 10.5244/c.27.8