Local Binary Patterns Calculated over Gaussian Derivative Images
| Autor: | James L. Crowley, Varun Jain, Augustin Lux |
|---|---|
| Přispěvatelé: | Perception, recognition and integration for observation of activity (PRIMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2014 |
| Předmět: |
Image derivatives
Local binary patterns Computer science business.industry Gaussian ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Binary pattern Facial recognition system Image (mathematics) symbols.namesake [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing 0202 electrical engineering electronic engineering information engineering symbols Benchmark (computing) Three-dimensional face recognition 020201 artificial intelligence & image processing Computer vision Artificial intelligence business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | ICPR ICPR 2014-22nd International Conference on Pattern Recognition ICPR 2014-22nd International Conference on Pattern Recognition, Aug 2014, Stockholm, Sweden. ⟨10.1109/ICPR.2014.683⟩ |
| DOI: | 10.1109/icpr.2014.683 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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