Our laboratory published an article in the international journal Expert Systems with Applications (Scopus, Q1):

Ryumina E., Markitantov M., Ryumin D., Karpov A. OCEAN-AI Framework with EmoFormer Cross-Hemiface Attention Approach for Personality Traits Assessment // Expert systems with applications, 2024, vol. 239, 122441.

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.