Multimodal First Impression Analysis with Deep Residual Networks - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Affective Computing Year : 2017

Multimodal First Impression Analysis with Deep Residual Networks

(1) , (1) , (2) , (3) , (4) , (2) , (1) , (1)
1
2
3
4

Abstract

People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities.
Not file

Dates and versions

hal-01668375 , version 1 (20-12-2017)

Identifiers

Cite

Yağmur Güçlütürk, Umut Güçlü, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon, et al.. Multimodal First Impression Analysis with Deep Residual Networks. IEEE Transactions on Affective Computing, 2017, PP (99), pp.1-14. ⟨10.1109/TAFFC.2017.2751469⟩. ⟨hal-01668375⟩
307 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More