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Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews
Journal of Real-Time Image Processing ( IF 3 ) Pub Date : 2021-01-27 , DOI: 10.1007/s11554-021-01071-5
Yu-Sheng Su , Hung-Yue Suen , Kuo-En Hung

This work aims to develop a real-time image and video processor enabled with an artificial intelligence (AI) agent that can predict a job candidate’s behavioral competencies according to his or her facial expressions. This is accomplished using a real-time video-recorded interview with a histogram of oriented gradients and support vector machine (HOG-SVM) plus convolutional neural network (CNN) recognition. Different from the classical view of recognizing emotional states, this prototype system was developed to automatically decode a job candidate’s behaviors by their microexpressions based on the behavioral ecology view of facial displays (BECV) in the context of employment interviews using a real-time video-recorded interview. An experiment was conducted at a Fortune 500 company, and the video records and competency scores were collected from the company’s employees and hiring managers. The results indicated that our proposed system can provide better predictive power than can human-structured interviews, personality inventories, occupation interest testing, and assessment centers. As such, our proposed approach can be utilized as an effective screening method using a personal-value-based competency model.



中文翻译:

通过实时视频采访中的面部表情自动预测行为能力

这项工作旨在开发一种具有人工智能(AI)代理的实时图像和视频处理器,该代理可以根据求职者的面部表情预测其应聘者的行为能力。这是通过使用实时视频录制的采访以及定向梯度直方图和支持向量机(HOG-SVM)加上卷积神经网络(CNN)识别来完成的。与识别情绪状态的经典观点不同,该原型系统的开发目的是根据求职面试中的面部表情的行为生态学观点(BECV),使用实时视频,根据求职者的微表情自动解码求职者的行为。记录的采访。在《财富》 500强公司中进行了一项实验,视频记录和能力得分是从公司的员工和招聘经理那里收集的。结果表明,我们提出的系统比人为结构的访谈,个性清单,职业兴趣测试和评估中心能够提供更好的预测能力。这样,我们提出的方法可以用作使用基于个人价值的能力模型的有效筛选方法。

更新日期:2021-01-28
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