%0 Conference Proceedings
%T Face identification based on synergism of classifiers in rectified stereo images
%D 2017
%A Carmo, Diedre,
%A Alves, Raul,
%A Oliveira, Luciano,
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%8 Oct. 17-20, 2017
%S Proceedings
%I Sociedade Brasileira de Computação
%J Porto Alegre
%K computer vision, stereo camera, face identification.
%X This paper proposes a method to identify faces from a stereo camera. Our approach tries to avoid common problems that come with using only one camera that shall arise while detecting from a relatively unstable view in real world applications. The proposed approach exploits the use of a local binary pattern (LBP) to describe the faces in each image of the stereo camera, after detecting the face using the Viola- Jones method. LBP histogram feeds then multilayer perceptron (MLP) and support vector machine classifiers to identify the faces detected in each stereo image, considering a database of target faces. Computational cost problem due to the use of dual cameras are alleviated with the use of co-planar rectified images, achieved through calibration of the stereo camera. Performance is assessed using the well established Yale face dataset, and performance is assessed by using only one or both camera images.
%@language en
%3 final-sibgrapi-wuw-tcc.pdf