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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/130163


    Title: TensorFlow-based Automatic Personality Recognition Used in Asynchronous Video Interviews
    Authors: 林建良
    Lin, Chien-Liang
    Suen, Hung-Yue
    Hung, Kuo-En
    Contributors: 資管博七
    Date: 2019-02
    Issue Date: 2020-06-22 10:47:32 (UTC+8)
    Abstract: With the development of artificial intelligence (AI), the automatic analysis of video interviews to recognize individual personality traits has become an active area of research and has applications in personality computing, human-computer interaction, and psychological assessment. Advances in computer vision and pattern recognition based on deep learning (DL) techniques have led to the establishment of convolutional neural network (CNN) models that can successfully recognize human nonverbal cues and attribute their personality traits with the use of a camera. In this study, an end-to-end AI interviewing system was developed using asynchronous video interview (AVI) processing and a TensorFlow AI engine to perform automatic personality recognition (APR) based on the features extracted from the AVIs and the true personality scores from the facial expressions and self-reported questionnaires of 120 real job applicants. The experimental results show that our AI-based interview agent can successfully recognize the "big five" traits of an interviewee at an accuracy between 90.9% and 97.4%. Our experiment also indicates that although the machine learning was conducted without large-scale data, the semisupervised DL approach performed surprisingly well with regard to automatic personality recognition despite the lack of labor-intensive manual annotation and labeling. The AI-based interview agent can supplement or replace existing self-reported personality assessment methods that job applicants may distort to achieve socially desirable effects.
    Relation: IEEE Access, 7, 61018 - 61023
    Data Type: article
    DOI link: https://doi.org/10.1109/ACCESS.2019.2902863
    DOI: 10.1109/ACCESS.2019.2902863
    Appears in Collections:[Department of MIS] Periodical Articles

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