Abstract
Appearance can affect social interaction, which in turn affects personality development. There is ample evidence that facial morphology and social cues provide information about human personality and behavior. In this study, we focused on the relationship between self-reported personality characteristics and facial features. We propose a new approach for predicting college students’ personality characteristics (on the basis of the Big Five personality characteristics) with static facial images. First, we construct a dataset containing 13,347 data pairs composed of facial images and personality characteristics. Second, we train a deep neural network with 10,667 sample pairs from the dataset and use the remaining samples to test (1335 pairs) and validate (1335 pairs) self-reported Big Five personalities. We trained a series of deep neural networks on a large, labeled dataset to predict the self-reported Big Five personality trait scores. This novel work applies deep learning to this topic. We also verify the network’s advanced nature on the publicly available database with obvious personality characteristics. The experimental results show that 1) personality traits can be reliably predicted from facial images with an accuracy that exceeds 70%. In five-character tag classification, the recognition accuracy of neuroticism and extroversion was the most accurate, and the prediction accuracy exceeded 90%. 2) Deep learning neural network features are better than traditional manual features in predicting personality characteristics. The results strongly support the application of neural networks trained on large-scale labeled datasets in multidimensional personality feature prediction from static facial images. 3) There are some differences in the personality traits of college students with different academic backgrounds. Future research can explore the relative contribution of other facial image features in predicting other personality characteristics.
Highlights
Since the time of the Greek philosopher Theophrastus (371–287 BC), people have been interested in the study of personality[1]
3) There are some differences in the personality traits of college students with different academic backgrounds
The traditional back propagation (BP) network is compared with the deep learning network, and an improved personality classification prediction network is proposed
Summary
Since the time of the Greek philosopher Theophrastus (371–287 BC), people have been interested in the study of personality[1]. Personality is a psychological structure that is designed to explain various human behaviors through a small number of stable and measurable individual characteristics[2]. Taking the "Big Five personality model" as an example, "love to make friends and be kind" indicates the personality type of "Extroversion"; the individual characteristics of "anxiety and emotional fragility" indicate the personality type of "Neuroticism". A growing number of studies have linked facial images to personality. It has been established that humans are able to perceive certain personality traits from each other’s faces with some degree of accuracy[3][4][5][6][7]. In addition to emotional expressions and other nonverbal behaviors that convey
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