Single sample face recognition from video via stacked supervised auto-encoder

Pedro J.Soto Vega, Raul Queiroz Feitosa, Victor H.Ayma Quirita, Patrick Nigri Happ

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


This work proposes and evaluates strategies based on Stacked Supervised Auto-Encoders (SSAE) for face representation in video surveillance applications. The study focuses on the identification task with a single sample per person (SSPP) in the gallery. Variations in terms of pose, facial expression, illumination and occlusion are approached in two ways. First, the SSAE extracts features from face images, which are robust to such variations. Second, we propose methods to exploit the multiple samples per persons probes (MSPPP) that can be extracted from video sequences. Three variants of the proposed method are compared upon HONDA/UCSD and VIDTIMIT video datasets. The experimental results demonstrate that strategies combining SSAE and MSPPP are able to outperform other SSPP methods, such a local binary patterns, in face recognition from video.

Original languageEnglish
Title of host publication2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
Place of PublicationUnited Estates
PublisherIEEE Xplore
Number of pages8
ISBN (Electronic)9781509035687
ISBN (Print)9781509035694
StatePublished - 16 Jan 2017
Externally publishedYes
EventSIBGRAPI Conference on Graphics, Patterns and Images - Sao Paulo, Brazil
Duration: 4 Oct 20167 Oct 2016
Conference number: 29

Publication series

NameBrazilian Symposium of Computer Graphic and Image Processing
ISSN (Print)1530-1834
ISSN (Electronic)2377-5416


ConferenceSIBGRAPI Conference on Graphics, Patterns and Images
Abbreviated titleSIBGRAPI
CitySao Paulo
Internet address

Bibliographical note

Publisher Copyright: © 2016 IEEE.

SIBGRAPI - Conference on Graphics, Patterns and Images is an international conference annually promoted by the Brazilian Computer Society (SBC).

Date Added to IEEE Xplore: 16 January 2017


  • Auto-encoder
  • Face Recognition
  • Surveillance


Dive into the research topics of 'Single sample face recognition from video via stacked supervised auto-encoder'. Together they form a unique fingerprint.

Cite this