Our laboratory published an article in the international journal Pattern Recognition Letters (Scopus, Q1):

Ryumina E., Markitantov M., Ryumin D., Karpov A. Gated Siamese Fusion Network based on Multimodal Deep and Hand-Crafted Features for Personality Traits Assessment // Pattern Recognition Letters. Elsevier, 2024, vol. 185, pp. 45-51 (WOS Q2 IF=3.9, Scopus SJR=1.4 Q1 AI)

People tend to judge others assessing their personality traits relying on life experience. This fact is especially evident when making an informed hiring decision, which should consider not only skills, but also match a company’s values and culture. Based on this assumption, we use the Siamese Network (SN) for assessing five personality traits by pairwise analyzing and comparing people simultaneously. For this, we propose the OCEAN-AI framework based on Gated Siamese Fusion Network (GSFN), which comprises six modules and enables the fusion of hand-crafted and deep features across three modalities (video, audio, and text). We use the ChaLearn First Impressions v2 (FIv2) and Multimodal Personality Traits Assessment (MuPTA) corpora and identify that all six feature sets and their combinations due to different information content allow the framework to adjust to heterogeneous input data flexibly. The experimental results show that the pairwise comparison of people with the same or different Personality Traits (PT) during the training enhances the proposed framework performance. The framework outperforms the State-of-the-Art (SOTA) systems based on three modalities (video-face, audio and text) by the relative value of 1.3% (0.928 vs. 0.916) in terms of the mean accuracy (mACC) on the FIv2 corpus. We also outperform the SOTA system in terms of the Concordance Correlation Coefficient (CCC) by the relative value of 8.6% (0.667 vs. 0.614) using two modalities (video and audio) on the MuPTA corpus. We make our framework publicly available to integrate it into various applications such as recruitment, education, and healthcare.