Deep Personality Trait Recognition: A Survey.

Area of Science:

• Computational psychology
• Neuropsychology
• Computer science

Background:

• Automatic personality trait recognition is a growing field.
• Deep learning methods have shown success in various tasks and are increasingly used for personality recognition.

Purpose of the Study:

• To systematically survey existing computational methods for automatic personality trait recognition.
• To provide an overview of available personality trait datasets.
• To review deep learning techniques and their application in this field.

Main Methods:

• Review of deep learning techniques: deep belief networks (DBNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
• Analysis of feature extraction methods: hand-crafted and deep learning-based.
• Examination of single and multi-modal approaches (audio, visual, text, physiological signals).

Main Results:

• Summarization of state-of-the-art personality trait recognition methods.
• Analysis of methods across different modalities.
• Identification of challenges and opportunities in the field.

Conclusions:

• Deep learning offers powerful tools for automatic personality trait recognition.
• Multi-modal approaches show promise for more accurate recognition.
• Further research is needed to address current challenges and explore future directions.