A Novel Multi-Level Pyramid Co-Variance ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
A Novel Multi-Level Pyramid Co-Variance Operators for Estimation of Personality Traits and Job Screening Scores
Auteur(s) :
Telli, Hichem [Auteur]
Sbaa, Salim [Auteur]
Bekhouche, Salah Eddine [Auteur]
Dornaika, Fadi [Auteur]
Universidad del Pais Vasco / Euskal Herriko Unibertsitatea [Espagne] [UPV/EHU]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
López, Miguel Bordallo [Auteur]
Sbaa, Salim [Auteur]
Bekhouche, Salah Eddine [Auteur]
Dornaika, Fadi [Auteur]
Universidad del Pais Vasco / Euskal Herriko Unibertsitatea [Espagne] [UPV/EHU]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
López, Miguel Bordallo [Auteur]
Titre de la revue :
Traitement du Signal
Pagination :
539-546
Éditeur :
Lavoisier
Date de publication :
2021
ISSN :
0765-0019
Mot(s)-clé(s) en anglais :
APA2016 dataset
Big-Five personality traits
job candidate screening
PML-COV descriptor
regression
Big-Five personality traits
job candidate screening
PML-COV descriptor
regression
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
Recently, automatic personality analysis is becoming an interesting topic for computer vision. Many attempts have been proposed to solve this problem using time-based sequence information. In this paper, we present a new ...
Lire la suite >Recently, automatic personality analysis is becoming an interesting topic for computer vision. Many attempts have been proposed to solve this problem using time-based sequence information. In this paper, we present a new framework for estimating the Big-Five personality traits and job candidate screening variable from video sequences. The framework consists of two parts: (1) the use of Pyramid Multi-level (PML) to extract raw facial textures at different scales and levels; (2) the extension of the Covariance Descriptor (COV) to fuse different local texture features of the face image such as Local Binary Patterns (LBP), Local Directional Pattern (LDP), Binarized Statistical Image Features (BSIF), and Local Phase Quantization (LPQ). Therefore, the COV descriptor uses the textures of PML face parts to generate rich low-level face features that are encoded using concatenation of all PML blocks in a feature vector. Finally, the entire video sequence is represented by aggregating these frame vectors and extracting the most relevant features. The exploratory results on the ChaLearn LAP APA2016 dataset compare well with state-of-the-art methods including deep learning-based methods.Lire moins >
Lire la suite >Recently, automatic personality analysis is becoming an interesting topic for computer vision. Many attempts have been proposed to solve this problem using time-based sequence information. In this paper, we present a new framework for estimating the Big-Five personality traits and job candidate screening variable from video sequences. The framework consists of two parts: (1) the use of Pyramid Multi-level (PML) to extract raw facial textures at different scales and levels; (2) the extension of the Covariance Descriptor (COV) to fuse different local texture features of the face image such as Local Binary Patterns (LBP), Local Directional Pattern (LDP), Binarized Statistical Image Features (BSIF), and Local Phase Quantization (LPQ). Therefore, the COV descriptor uses the textures of PML face parts to generate rich low-level face features that are encoded using concatenation of all PML blocks in a feature vector. Finally, the entire video sequence is represented by aggregating these frame vectors and extracting the most relevant features. The exploratory results on the ChaLearn LAP APA2016 dataset compare well with state-of-the-art methods including deep learning-based methods.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Source :
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