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Psychological and neurological studies earlier suggested that a personality type can be determined by the whole face as well as by its sides. This article discusses novel research using deep neural networks that address the features of both sides of the face (hemifaces) to assess the human's Big Five personality traits (PT). For this, we have developed a real-time approach called EmoFormer with cross-hemiface attention. The novelty of the presented approach lies in the confirmation that each hemiface exhibits high predictive capabilities in terms of human's PT distinction. Our approach is based on a novel mid-level emotional feature extractor for each hemiface and a cross-hemiface attention fusion strategy for hemiface feature aggregation. The consequent fusion of both hemifaces has outperformed the use of the whole face by the relative value of 3.6% in terms of Concordance Correlation Coefficient (0.634 vs. 0.612) on the ChaLearn First Impressions V2 corpus. The proposed approach has also outperformed all the existing state-of-the-art approaches for PT assessment based on the face modality. We have also analyzed the "best hemiface", the one that predicts PT more accurately in terms of demographic characteristics (gender, ethnicity, and age). We have found that the best hemiface for two of the five PT (Openness to experience and Non-Neuroticism) is different depending on demographic characteristics. For the other three traits, the right hemiface is dominant for Extraversion, while the left one is more indicative of Conscientiousness and Agreeableness. These findings support previous psychological and neurological research. Besides, we provide an open-source framework referred to as OCEAN-AI that can be seamlessly integrated into expert systems with practical applications in various domains including healthcare, education, and human resources. • An efficient EmoFormer cross-hemiface attention approach for Big five assessment. • A novel emotional mid-level feature extractor for personality traits assessment. • A novel cross-hemiface attention fusion strategy for personality traits assessment. • An open-source framework called OCEAN-AI for personality traits assessment. [ABSTRACT FROM AUTHOR]

• The data were collected on big five personality traits using the Big Five Inventory (BFI) • Driver Behavior Questionnaire (DBQ) was used to collect data on self-reported driving behaviors. • Lisrel software was used to conduct the latent variable model. • Personality traits in combination with age, gender, and the history of accident involvement were used as the predictors of aberrant driving behaviors. • It was suggested to the policymakers to introduce the personality test in the driver correction programme. Introduction: This study aims to explore the influence of Big Five personality traits in combination with various socio-demographic factors and experiences of accident involvement on aberrant driving behaviors. The study also compares the effects of the level of development (i.e., developed or developing) of three countries on the personality traits and driving behaviors. Method: The four-factor Driver Behavior Questionnaire was used to collect data on aberrant driving behaviors, while a short version of the 10-item Big Five Inventory was used to collect data on personality traits. Responses were collected from Japan (1,250 responses), China (1,250), and Vietnam (1,000). A latent variable model was applied after controlling data in each category (e.g., age). Results: This study revealed that respondents who experienced accidents in the past and scored higher on Agreeableness were less likely to commit aggressive violations in Japan, China, and Vietnam. Further, Japanese and Vietnamese female drivers who scored high on Conscientiousness were found to be less likely to commit ordinary violations. Neuroticism was positively correlated with aggressive violations only in the case of Vietnamese drivers, irrespective of the history of accident involvement. Conclusions: Drivers with particular personality types that are linked with aberrant driving behavior may need to receive additional training on behavior management. Practical Applications: This study may help road traffic policymakers predict future driving behaviors of Vietnamese and Chinese drivers based on those of Chinese and Japanese drivers, respectively, and act accordingly. [ABSTRACT FROM AUTHOR]