University of Oulu

Telli, H., Sbaa, S., Bekhouche, S.E., Dornaika, F., Taleb-Ahmed, A., López, M.B. (2021). A novel multi-level Pyramid Co-Variance operators for estimation of personality traits and job screening scores. Traitement du Signal, Vol. 38, No. 3, pp. 539-546. https://doi.org/10.18280/ts.380301

A novel multi-level pyramid co-variance operators for estimation of personality traits and job screening scores

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Author: Telli, Hichem1; Sbaa, Salim1; Bekhouche, Salah Eddine2;
Organizations: 1Laboratory of LESIA, University of Biskra, Biskra 07000, Algeria
2Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Leioa 48940, Spain
3IKERBASQUE, Basque Foundation for Science, Bilbao 48009, Spain
4UniversitéPolytechnique Hauts de France, Univ. Lille, CNRS, Centrale Lille, F-59313, Valenciennes, France
5VTT Technical Research Centre of Finland & University of Oulu, Oulu 90570, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021100750158
Language: English
Published: International Information and Engineering Technology Association, 2021
Publish Date: 2021-10-07
Description:

Abstract

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.

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Series: Traitement du signal
ISSN: 0765-0019
ISSN-E: 1958-5608
ISSN-L: 0765-0019
Volume: 38
Issue: 3
Pages: 539 - 546
DOI: 10.18280/ts.380301
OADOI: https://oadoi.org/10.18280/ts.380301
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Copyright information: All articles in Traitement du Signal are published under open access. IIETA will hold copyright on all papers, while the author will maintain all other rights including patents and the right to use and reproduce material.