A Novel Multi-Level Pyramid Co-Variance Operators for Estimation of Personality Traits and Job Screening Scores
| Autor: | Salim Sbaa, Hichem Telli, Miguel Bordallo López, Abdelmalik Taleb-Ahmed, Salah Eddine Bekhouche, Fadi Dornaika |
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| Přispěvatelé: | Université Bourgogne Franche-Comté [COMUE] (UBFC), Universidad del Pais Vasco / Euskal Herriko Unibertsitatea [Espagne] (UPV/EHU), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
PML-COV descriptor
Big-Five personality traits Local binary patterns Computer science business.industry Feature vector Deep learning Frame (networking) Concatenation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Job candidate screening Covariance Regression [SPI]Engineering Sciences [physics] APA2016 dataset job candidate screening Face (geometry) regression Artificial intelligence Pyramid (image processing) Electrical and Electronic Engineering business |
| Zdroj: | Traitement du Signal Traitement du Signal, 2021, 38 (3), pp.539-546. ⟨10.18280/ts.380301⟩ Traitement du Signal, Lavoisier, 2021, 38 (3), pp.539-546. ⟨10.18280/ts.380301⟩ 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 |
| ISSN: | 0765-0019 1958-5608 |
| DOI: | 10.18280/ts.380301⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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