Deep attributes for one-shot face recognition

Aishwarya Jadhav, Vinay P. Namboodiri, K. S. Venkatesh

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

9 Citations (SciVal)


We address the problem of one-shot unconstrained face recognition. This is addressed by using a deep attribute representation of faces. While face recognition has considered the use of attribute based representations, for one-shot face recognition, the methods proposed so far have been using different features that represent the limited example available. We postulate that by using an intermediate attribute representation, it is possible to outperform purely face based feature representation for one-shot recognition. We use two one-shot face recognition techniques based on exemplar SVM and one-shot similarity kernel to compare face based deep feature representations against deep attribute based representation. The evaluation on standard dataset of ‘Labeled faces in the wild’ suggests that deep attribute based representations can outperform deep feature based face representations for this problem of one-shot face recognition.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2016 Workshops, Proceedings
EditorsGang Hua, Herve Jegou
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319494081
Publication statusPublished - 1 Jan 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9915 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th European Conference on Computer Vision, ECCV 2016


  • Attributes
  • Face recognition
  • One-shot classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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