Face Classification using a New Local Texture Descriptor

  • Carolina Toledo Ferraz USP
  • Marcelo Garcia Manzato USP
  • Adilson Gonzaga USP


Face recognition has received significant attention during the past several years. It is a challenge task because faces can be affected by scale, noises, face expression, illumination, color or pose variations. The most robust methodologies related to these variations are based on “key points” localization, followed by the application of a local descriptor to each surrounding region. Such descriptors are associated to clustering algorithms or histogram representation based on Bag of Features (BoF). In the BoF approach, the codebook can effectively describe objects by their appearance based on local texture. Based on texture descriptors proposed previously for image detection, we propose in this paper the application of such descriptors for face recognition. We evaluate the performance of our methodology using Feret, ORL and Yale databases, comparing our descriptor against SIFT and LIOP descriptors, and also other methodologies recently published in the literature.
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FERRAZ, Carolina Toledo; MANZATO, Marcelo Garcia; GONZAGA, Adilson. Face Classification using a New Local Texture Descriptor. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 23. , 2017, Gramado. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2017 . p. 237-240.

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