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.

中文翻译：

---

CR-Net：用于多模态表观人格分析的深度分类回归网络

第一印象强烈影响社交互动，对个人和职业生活产生重大影响。在本文中，我们提出了一个深度分类回归网络 (CR-Net)，用于分析大五人格问题，并在第一印象设置中进一步协助求职面试推荐。该设置基于 ChaLearn 第一印象数据集，包括从相应的音频数据转换而来的视频、音频和文本的多模态数据，其中每个人都在摄像机前交谈。为了给出全面的预测，我们分析了来自整个场景（包括人的动作和背景）和人脸的视频。我们的 CR-Net 首先进行个性特征分类，然后应用回归，可以对性格特征和面试推荐进行准确的预测。此外，我们提出了一种称为 Bell Loss 的新损失函数，以解决由回归均值问题引起的不准确预测。对 First Impressions 数据集的大量实验显示了我们提出的网络的有效性，优于最先进的网络。