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Personality Recognition Models

In: Multimodal Affective Computing

Author

Listed:
  • Ramón Zatarain Cabada

    (Instituto Tecnológico de Culiacán)

  • Héctor Manuel Cárdenas López

    (Instituto Tecnológico de Culiacán)

  • Hugo Jair Escalante

    (Instituto Nacional de Astrofísica)

Abstract

This chapter focuses on the various classification models for personality recognition, including each classifier’s feature selection process and personality traits. It also explores the reasoning behind each classifier architecture and the common challenges in creating classification models for personality. The chapter covers image-based models, such as convolutional neural networks, residual networks, long short-term memory implementations, and sound-based models that use residual and long short-term memory implementations. The chapter also discusses some multimodal models and the challenges associated with each model subset. Overall, the chapter aims to provide readers with a comprehensive understanding of DL techniques that can be used for human personality recognition, with different implementations for different modalities, and to present multiple modality models for personality recognition.

Suggested Citation

  • Ramón Zatarain Cabada & Héctor Manuel Cárdenas López & Hugo Jair Escalante, 2023. "Personality Recognition Models," Springer Books, in: Multimodal Affective Computing, chapter 0, pages 167-171, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-32542-7_14
    DOI: 10.1007/978-3-031-32542-7_14
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