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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

11 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
Lugar de publicaciónUnited Estates
EditorialIEEE Xplore
Páginas96-103
Número de páginas8
ISBN (versión digital)9781509035687
ISBN (versión impresa)9781509035694
DOI
EstadoPublicada - 16 ene. 2017
Publicado de forma externa
EventoSIBGRAPI Conference on Graphics, Patterns and Images - Sao Paulo, Brasil
Duración: 4 oct. 20167 oct. 2016
Número de conferencia: 29
http://gibis.unifesp.br/sibgrapi16/

Serie de la publicación

NombreBrazilian Symposium of Computer Graphic and Image Processing
ISSN (versión impresa)1530-1834
ISSN (versión digital)2377-5416

Conferencia

ConferenciaSIBGRAPI Conference on Graphics, Patterns and Images
Título abreviadoSIBGRAPI
País/TerritorioBrasil
CiudadSao Paulo
Período4/10/167/10/16
Dirección de internet

Nota bibliográfica

Publisher Copyright: © 2016 IEEE.

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