Local Binary Patterns Calculated Over Gaussian Derivative Images - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Local Binary Patterns Calculated Over Gaussian Derivative Images

(1) , (1) , (1)
1

Abstract

In this paper we present a new static descriptor for facial image analysis. We combine Gaussian derivatives with Local Binary Patterns to provide a robust and powerful descriptor especially suited to extracting texture from facial images. Gaussian features in the form of image derivatives form the input to the Linear Binary Pattern(LBP) operator instead of the original image. The proposed descriptor is tested for face recognition and smile detection. For face recognition we use the CMU-PIE and the YaleB+extended YaleB database. Smile detection is performed on the benchmark GENKI 4k database. With minimal machine learning our descriptor outperforms the state of the art at smile detection and compares favourably with the state of the art at face recognition.
Fichier principal
Vignette du fichier
5209d987.pdf (438.85 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

hal-01061099 , version 1 (05-09-2014)

Identifiers

Cite

Varun Jain, James L. Crowley, Augustin Lux. Local Binary Patterns Calculated Over Gaussian Derivative Images. ICPR 2014 - 22nd International Conference on Pattern Recognition, Aug 2014, Stockholm, Sweden. ⟨10.1109/ICPR.2014.683⟩. ⟨hal-01061099⟩
238 View
488 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More