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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]

Identifying personality traits in children is a topic of interest due to its importance in adapting strategies for the teaching–learning process and detecting psychopathological features. The most straightforward procedure to identify children's personality type is by applying a validated and suitable questionnaire according to their age. However, an interesting approach is automatically identifying the children's personalities using their speech generated during their interaction with computer systems, software, or robots. This approach would allow obtaining the personality identification transparently to the children without answering a written test. This article presents a method for the automatic personality assessment of children between 8 and 12 years old. The assessment is based on their voices' acoustic analysis while participating in playful activity with another child and a robot. We created a database with 98 children involved in several activities while their voices were recorded. The database was labeled with five paralinguistic aspects. Using these labels, we trained a set of classification models that helped us recognize primary and secondary personality traits according to the Children's Personality Questionnaire. We obtained good results for two secondary personality traits, extroversion (0.89 F-Score) and excitability (0.79 F-Score). The best F-score obtained for anxiety was 0.70. These results indicate that it is feasible to estimate personality from analyzing children's voices during interaction with computer systems. • We developed a system to analyze of children's speech to infer their personality. • The children's actual personality was obtained by applying a personality test. • The obtained accuracy is similar to that obtained in studies with adults. • The developed system can be used in personality aware systems to profile users. [ABSTRACT FROM AUTHOR]