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

Hichem Telli (Corresponding Author), Salim Sbaa, Salah Eddine Bekhouche, Fadi Dornaika, Abdelmalik Taleb-Ahmed, Miguel Bordallo López

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Pages (from-to)539-546
Number of pages8
JournalTraitement du Signal
Volume38
Issue number3
DOIs
Publication statusPublished - Jun 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • APA2016 dataset
  • Big-Five personality traits
  • Job candidate screening
  • PML-COV descriptor
  • Regression

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