Scopus: 1 citas, Google Scholar: citas
Recognition of Facial Expressions using Local Mean Binary Pattern
Goyani, Mahesh M. (Charotar University of Science and Technology (Changa, Índia). Department of Computer Engineering)
Patel, Narendra (BVM Engineering College (Índia). Department of Computer Engineering)

Fecha: 2017
Resumen: In this paper, we propose a novel appearance based local feature extraction technique called Local Mean Binary Pattern (LMBP), which efficiently encodes the local texture and global shape of the face. LMBP code is produced by weighting the thresholded neighbor intensity values with respect to mean of 3 x 3 patch. LMBP produces highly discriminative code compared to other state of the art methods. The micro pattern is derived using the mean of the patch, and hence it is robust against illumination and noise variations. An image is divided into M x N regions and feature descriptor is derived by concatenating LMBP distribution of each region. We also propose a novel template matching strategy called Histogram Normalized Absolute Difference (HNAD) for comparing LMBP histograms. Rigorous experiments prove the effectiveness and robustness of LMBP operator. Experiments also prove the superiority of HNAD measure over well-known template matching methods such as L2 norm and Chi-Square measure. We also investigated LMBP for facial expression recognition low resolution. The performance of the proposed approach is tested on well-known datasets CK, JAFFE, and TFEID.
Derechos: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Lengua: Anglès.
Documento: article ; recerca ; publishedVersion
Materia: Local binary pattern ; Local mean binary pattern ; Local direction pattern ; Histogram normalized absolute difference ; Support vector machine
Publicado en: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 16 Núm. 1 (2017) , p. 54-67 (Regular Issue) , ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.1058

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