Paper Info
Title
Don't ask me what i'm like, just watch and listen
Abstract
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).
Year
DOI
Venue
2012
10.1145/2393347.2393397
ACM Multimedia 2001
Keywords
Field
DocType
personality assessment,personality score,novel psychology-based audio,personality questionnaire,extracted feature,novel regression model,bfi-10 scoring scheme,visual feature,bfi-10 answer,big-five inventory-10,movie analysis
Kernel (linear algebra),Ask price,Regression analysis,Emotion recognition,Personality Assessment Inventory,Computer science,Multimedia,Personality,CLIPS
Conference
Citations 
PageRank 
References 
13
0.73
13
Authors
5
Name
Order
Citations
PageRank
Ruchir Srivastava1474.18
Jiashi Feng22165140.81
Sujoy Roy316917.35
Shuicheng Yan4197074.15
Terence Sim52562169.42