Affective computing
Convex Optimization
Fully Automatic Facial Action Recognition in Spontaneous Behavior
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Whose thumb is it anyway?: classifying author personality from weblog text
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Multimodal recognition of personality traits in social interactions
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
Space speaks: towards socially and personality aware visual surveillance
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
The voice of personality: mapping nonverbal vocal behavior into trait attributions
Proceedings of the 2nd international workshop on Social signal processing
Video scene segmentation using Markov chain Monte Carlo
IEEE Transactions on Multimedia
Emotion Recognition in Text for 3-D Facial Expression Rendering
IEEE Transactions on Multimedia
Multimedia Tools and Applications
A Connotative Space for Supporting Movie Affective Recommendation
IEEE Transactions on Multimedia
Automatic user preference elicitation for music recommendation
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Hi-index | 0.00 |
Traditional (based on psychology) approaches for personality assessment of an individual require him/her to fill up a questionnaire. This paper presents a novel way of utilizing multimodal cues to automatically fill up the questionnaire. The contributions of this work are three-fold. (1) Novel psychology-based audio/visual/lexical features are proposed and shown to be effective in predicting answers to a personality questionnaire, Big-Five Inventory-10 (BFI- 10). (2) Extracted features are used to learn linear and kernel versions of a novel regression model, 'SLoT', to automatically predict BFI-10 answers. The model is based on Sparse and Low-rank Transformation (SLoT). (3) Predicted answers are used to compute personality scores using standard BFI-10 scoring scheme. We evaluated our approach on a dataset of 3907 clips (for 50 characters from movies of diverse genres) manually labeled with BFI-10 answers and personality scores as ground-truth. Experiments indicate that the proposed 'SLoT' model effectively automates the answering process by emulating human understanding. We also conclude that predicting personality scores through predicting answers first is better than directly predicting scores based on audio/visual features (as studied in state-of-the art methods).