TY  - JOUR
AU  - Yunan Li
AU  - Jun Wan
AU  - Qiguang Miao
AU  - Sergio Escalera
AU  - Huijuan Fang
AU  - Huizhou Chen
AU  - Xiangda Qi
AU  - Guodong Guo
PY  - 2020//
TI  - CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis
T2  - IJCV
JO  - International Journal of Computer Vision
SP  - 2763–2780
VL  - 128
N2  - First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art.
UR  - https://link.springer.com/article/10.1007/s11263-020-01309-y
N1  - HuPBA; no menciona
ID  - Yunan Li2020
ER  -