企業想要挑選高績效人才, 根本之道是透過有信度與效度的甄選工具。傳統的甄選流程大多透過履歷表篩選、面談評測應徵者的職能是否符合該職缺的需求。
研究發現，應徵者的過去行為(Past Behaviour)是預測未來行為(Future Behaviour)與績效(Job Performance)最有效的預測因子，但行為事例式面談需耗費人力且缺乏效率。在心理學的領域發現，一個人在特定情境中的微表情除了反應當事人的情緒狀態外，也能用來預測當事人的未來行為傾向，從下一秒到下一年都有可能。隨著電腦視覺(Computer Vision)以及深度學習(Deep Learning)技術的發展，心理學家開始與電腦科技領域的專家合作，透過視訊記錄辨識當事人的微表情並用來推測當事人的未來行為。其中尤以卷積神經網絡(Convolutional Neural Network, CNN)，是目前最廣泛被應用在微表情分析的深度學習技術。本研究採用實證研究法，研究對象為個案公司中101位企業內部具業務工作性質員工。結果顯示，可以運用電腦視覺處理技術蒐集業務工作性質員工在特定環境中（模擬求職面試）回答特定問題所表現出來的面部微表情運動軌跡，以卷積神經網絡建立微表情與工作績效模型，具有91 %的機率可以推測其在工作績效的考核結果，提供企業選才時另一項快速且有效的甄選決策輔助工具。

To select high-performance workforce, the fundamental way is to use selection tools based on the principle of credibility and validity. The traditional selection process mostly uses resume screening and interviews to evaluate whether candidate meet the job requirements.
Studies found that job applicants’ past behaviors can predict future related behaviors and job performance. However, behavioral event interviews are labor-intensive and inefficient. In the field of psychology, it was found that a person’s Micro-Expressions in a specific situation can not only reflect the person’s emotional state, but also be used to predict the person’s future behavior tendency-to next second or even to next year. With the development of Computer Vision and Deep Learning technology (e.g. Convolutional Neural Network, CNN), psychologists are collaborating with computer science experts to decode targets Micro-Expressions through video recordings and use them to predict their behavior as well as emotion states. This research was conducted in a field environment, , and solicited 101 salespersons as participants from a company. The results show that sales representatives’ micro-expressions can be retrieved from mock job interviews in asynchronous video platform,, and the motional features of micro-expressions are associated with the targets’ job performance appraisals based the CNN modeling. Supported by accuracy levels of 91%, the study proves that a job interviewee’s micro-expressions can infer his or her job performance appraisal evaluated by his or her supervisor, and the proposed methodology can be used as an alternative personnel assessment tool for employment screening with more efficient and valid compared with the other traditional selection methods.

余雅屏（2003）。人格特質、自我導向學習以及工作績效之相關性研究（碩士論文）。國立中山大學。
周萍芬、蔡亞純（2004）。企業之員工績效評估研究─個案公司之探討。遠東學報，21（1），169–176。http://www.feu.edu.tw/adms/aao/aao95/jfeu/21/210116.pdf
彭巍（2010）。員工情緒智力與工作績效關係研究（博士論文）。中南大學。
陳建丞、蔡維奇（2005）。面談前印象對面試官評量效應之探討：以面談結構性爲干擾變數。臺大管理論叢，16（1），155–170。http://doi.org/10.6226/NTURM2005.16.1.155
陳素雅（2012）。情緒智力與工作績效之關係-探討工作動機的中介影響（碩士論文）。國立中央大學。
堯翊淇（2020）。人工智慧模擬面試官預測應徵者溝通技巧與五大性格之效果研究（碩士論文）。國立臺灣師範大學。

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., ..., Zheng, X. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. ArXiv, arXiv:1603.04467. https://arxiv.org/pdf/1603.04467.pdf
Adouani, A., Henia, W. M. B., & Lachiri, Z. (2019, March). Comparison of Haar-like, HOG and LBP approaches for face detection in video sequences. 2019 16th International Multi-Conference on Systems, Signals & Devices, 266–271. http://doi.org/10.1109/SSD.2019.8893214
Aslan, M. F., Durdu, A., Sabanci, K., & Mutluer, M. A. (2020). CNN and HOG based comparison study for complete occlusion handling in human tracking. Measurement, 158, 107704. https://doi.org/10.1016/j.measurement.2020.107704
Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M. (2013). Robust discriminative response map fitting with constrained local models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3444–3451. https://openaccess.thecvf.com/content_cvpr_2013/html/Asthana_Robust_Discriminative_Response_2013_CVPR_paper.html
Barrett, Lisa Feldman, Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in The Public Interest, 20(1), 1–68. https://doi.org/10.1177/1529100619832930
Birdwhistell, R. L. (1970). Kinesics and context. University of Pennsylvania press.
Blum, L., Dieckmann, A., & Unfried, M. (2020). Confusion can improve cognitive performance: An experimental study using automatic facial expression analysis. NIM Working Paper Series, 8.
Borman, Walter C., & Motowidlo, S. M. (1993). Expanding the criterion domain to include elements of contextual performance. In N. Schmitt & W. C. Borman (Ed.), Personnel selection in organizations (pp. 71–98). Psychology Faculty Publications.
Bregar, K., & Mohorčič, M. (2018). Improving indoor localization using convolutional neural networks on computationally restricted devices. IEEE Access, 6, 17429–17441. https://doi.org/10.1109/ACCESS.2018.2817800
Brief, A. P., & Weiss, H. M. (2002). Organizational behavior: Affect in the workplace. Annual Review of Psychology, 53(1), 279–307. https://doi.org/10.1146/annurev.psych.53.100901.135156
Burges, Christopher John, & John Stewart Denker (1998). System and method for automated interpretation of input expressions using novel a posteriori probability measures and optimally trained information processing networks. Google Patents.
Campbell, J. P. (1990). Modeling the performance prediction problem in industrial and organizational psychology. In M. D. Dunnette & L. M. Hough (Ed.), Handbook of industrial and organizational psychology (pp. 687–732). Consulting Psychologists Press.
Campion, M. A., Campion, J. E., & Hudson, J. P., Jr. (1994). Structured interviewing: A note on incremental validity and alternative question types. Journal of Applied Psychology, 79(6), 998–1002. https://doi.org/10.1037/0021-9010.79.6.998
Carcagnì, P., Del Coco, M., Leo, M., & Distante, C. (2015). Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus, 4(1), 1–25. https://doi.org/10.1186/s40064-015-1427-3
Cardy, R. L., & Selvarajan, T. T. (2006). Competencies: Alternative frameworks for competitive advantage. Business Horizons, 49(3), 235–245. https://doi.org/10.1016/j.bushor.2005.09.004
Celiktutan, O., & Gunes, H. (2015). Automatic prediction of impressions in time and across varying context: Personality, attractiveness and likeability. IEEE Transactions on Affective Computing, 8(1), 29–42. https://doi.org/10.1109/TAFFC.2015.2513401
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active appearance models. In Burkhardt H., Neumann B. (Ed.), Computer Vision—ECCV’98 (pp.484–498). Springer, Berlin, Heidelberg.
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681–685. https://doi.org/10.1109/34.927467
Conrad, D., & Newberry, R. (2011). 24 Business Communication Skills: Attitudes of Human Resource Managers versus Business Educators. American Communication Journal, 13(1), 4–23. http://www.ac-journal.org/journal/pubs/2011/spring/ConradNewberry.pdf
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models-their training and application. Computer Vision and Image Understanding, 61(1), 38–59. https://doi.org/10.1006/cviu.1995.1004
Crivelli, C., Carrera, P., & Fernández-Dols, J. M. (2015). Are smiles a sign of happiness? Spontaneous expressions of judo winners. Evolution and Human Behavior, 36(1), 52–58. https://doi.org/10.1016/j.evolhumbehav.2014.08.009
Crivelli, C., & Fridlund, A. J. (2018). Facial displays are tools for social influence. Trends in Cognitive Sciences, 22(5), 388–399. https://doi.org/10.1016/j.tics.2018.02.006
Dalal, N., & Triggs, B. (2005, June). Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 886–893. https://doi.org/10.1109/CVPR.2005.177
DeGroot, T., & Gooty, J. (2009). Can nonverbal cues be used to make meaningful personality attributions in employment interviews?. Journal of Business and Psychology, 24(2), 179–192. https://doi.org/10.1007/s10869-009-9098-0
Deveugele, M. (2015). Communication training: skills and beyond. Patient Education and Counseling, 98(10), 1287–1291. https://doi.org/10.1016/j.pec.2015.08.011
Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18(2), 192. https://doi.org/10.1037/1040-3590.18.2.192
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Ekman, P., & Friesen, W. V. (1978). Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists Press.
Ekman, P. (2003). Emotions inside out. 130 Years after Darwin's" The Expression of the Emotions in Man and Animal". Annals of the New York Academy of Sciences, 1000, 1–6. https://doi.org/10.1196/annals.1280.002
Ekman, P. (2009). Lie catching and microexpressions. The philosophy of deception.
Ekman, P., & Friesen, W. V. (2010). The Repertoire of Nonverbal Behavior: Categories, Origins, Usage, and Coding. In A. Kendon (Ed.), Nonverbal communication, interaction, and gesture: selections from SEMIOTICA (pp. 57–106). De Gruyter Mouton. https://doi.org/10.1515/9783110880021.57
Escalante, H. J., Kaya, H., Salah, A. A., Escalera, S., Güçlütürk, Y., Güçlü, U., Baró, X., Guyon, I., Júnior, J. C. S. J., Madadi, M., Ayache, S., Viegas, E., Gürpinar, F., Wicaksana, A. S., Liem, C. C. S., Gerven, M. A. J. V., & Lier, R. V. (2018). Explaining First Impressions-Modeling, Recognizing, and Explaining Apparent Personality from Videos. ArXiv, abs/1802.00745. https://dblp.org/rec/journals/corr/abs-1802-00745
Fleiss, J. L. (1986). Significance tests have a role in epidemiologic research: reactions to AM Walker. American Journal of Public Health, 76(5), 559–560. https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.76.5.559
Freitas-Magalhães, A. (2020). The psychology of emotions-the allure of human face (50th Ed.). Leya.
Fridlund, A. J. (2017). The behavioral ecology view of facial displays, 25 years later. The science of facial expression.
Furnham, A. (1992). Personality and learning style: A study of three instruments. Personality and Individual Differences, 13(4), 429–438. https://doi.org/10.1016/0191-8869(92)90071-V
Gatewood, R. D., & Hubert, S. (1998). Human Resource Selection. The Dryden Press: Dryden.
Gottman, J. M., & Levenson, R. W. (2002). A two‐factor model for predicting when a couple will divorce: Exploratory analyses using 14‐year longitudinal data. Family Process, 41(1), 83–96. https://doi.org/10.1111/j.1545-5300.2002.40102000083.x
Green, P. C., Alter, P., & Carr, A. F. (1993). Development of standard anchors for scoring generic past‐behaviour questions in structured interviews. International Journal of Selection and Assessment, 1(4), 203–212. https://doi.org/10.1111/j.1468-2389.1993.tb00114.x
Guion, R. M., & Highhouse, S. (2006). Essentials of personnel assessment and selection. ‎ Psychology Press.
Hammal, Z., Couvreur, L., Caplier, A., & Rombaut, M. (2007). Facial expression classification: An approach based on the fusion of facial deformations using the transferable belief model. International Journal of Approximate Reasoning, 46(3), 542–567. https://doi.org/10.1016/j.ijar.2007.02.003.
Hartwell, C. J., Johnson, C. D., & Posthuma, R. A. (2019). Are we asking the right questions? Predictive validity comparison of four structured interview question types. Journal of Business Research, 100, 122–129. https://doi.org/10.1016/j.jbusres.2019.03.026
Huffcutt, A. I., Van Iddekinge, C. H., & Roth, P. L. (2011). Understanding applicant behavior in employment interviews: A theoretical model of interviewee performance. Human Resource Management Review, 21(4), 353–367. https://doi.org/10.1016/j.hrmr.2011.05.003
Hurtz, G. M., & Donovan, J. J. (2000). Personality and job performance: The Big Five revisited. Journal of Applied Psychology, 85(6), 869–879. https://doi.org/10.1037/0021-9010.85.6.869
J Janz, T. (1982). Initial comparisons of patterned behavior description interviews versus unstructured interviews. Journal of Applied Psychology, 67(5), 577–580. https://doi.org/10.1037/0021-9010.67.5.577
Johnston, B., & de Chazal, P. (2018). A review of image-based automatic facial landmark identification techniques. EURASIP Journal on Image and Video Processing, 2018(1), 1–23. https://doi.org/10.1186/s13640-018-0324-4
Jones, J. W., Brasher, E. E., & Huff, J. W. (2002). Innovations in integrity‐based personnel selection: building a technology‐friendly assessment. International Journal of Selection and Assessment, 10(1–2), 87–97. https://doi.org/10.1111/1468-2389.00195
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/255-260. 10.1126/science.aaa8415
Knapp, Mark L. (1972). Nonverbal communication in human interaction. Holt, Rinehart and Winston.
Knapp, M. L., Hall, M. A., & Horgan, T. G. (2013). Nonverbal communication in human interaction. Cengage Learning.
Korman, Abraham K. (1977). Organizational behavior. Prentice Hall.
Kwan, Y.M., & Kim, J.S. (2009). Effect of the Communication Skill of Organization on Their Job Performance. Journal of Digital Convergence., 7(4), 141–148. https://www.dbpia.co.kr/pdf/cpViewer
Kwon, Y. M., & Lee, J. H. (2010). On the effect of the communication skill of organization on their job performance. The Korean Society for Quality Management, 2010, 183–190, https://papersearch.net/thesis/article.asp?key=3047707
Larson, L. L., & Schermerhorn Jr, J. R. (1989). Alternative instructor roles in cross-cultural business and management training. Journal of Teaching in International Business, 1(1), 7–21. https://doi.org/10.1300/J066v01n01_02
Latham, G. P., Saari, L. M., Pursell, E. D., & Campion, M. A. (1980). The situational interview. Journal of Applied Psychology, 65(4), 422–427. https://doi.org/10.1037/0021-9010.65.4.422
Latham, G. P., & Saari, L. M. (1984). Do people do what they say? Further studies on the situational interview. Journal of Applied Psychology, 69(4), 569–573. https://doi.org/10.1037/0021-9010.69.4.569
Leary, M. R., & Kowalski, R. M. (1990). Impression management: A literature review and two-component model. Psychological Bulletin, 107(1), 34–47. https://doi.org/10.1037/0033-2909.107.1.34
Levashina, J., & Campion, M. A. (2006). A model of faking likelihood in the employment interview. International Journal of Selection and Assessment, 14(4), 299–316. https://doi.org/10.1111/j.1468-2389.2006.00353.x
Lishman, J. (1994). Helpful and Effective Communication: Our Clients’ Views. In Communication in Social Work (pp. 6–14). Palgrave.
Liu, Y. J., Zhang, J. K., Yan, W. J., Wang, S. J., Zhao, G., & Fu, X. (2015). A main directional mean optical flow feature for spontaneous micro-expression recognition. IEEE Transactions on Affective Computing, 7(4), 299–310. https://doi.org/10.1109/TAFFC.2015.2485205
Matsugu, M., Mori, K., Mitari, Y., & Kaneda, Y. (2003). Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Networks, 16(5–6), 555–559. https://doi.org/10.1016/S0893-6080(03)00115-1
McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1(1), 30–46. https://doi.org/10.1037/1082-989X.1.1.30
McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22(3), 276–282. https://hrcak.srce.hr/89395
McLarnon, M. J., DeLongchamp, A. C., & Schneider, T. J. (2019). Faking it! Individual differences in types and degrees of faking behavior. Personality and Individual Differences, 138, 88–95. https://doi.org/10.1016/j.paid.2018.09.024
Mehrabian, R. (1982). Rapid solidification. International Metals Reviews, 27(1), 185–208. https://doi.org/10.1179/imr.1982.27.1.185
Melchers, K. G., Roulin, N., & Buehl, A. K. (2020). A review of applicant faking in selection interviews. International Journal of Selection and Assessment, 28(2), 123–142. https://doi.org/10.1111/ijsa.12280
Merget, D., Rock, M., & Rigoll, G.(2018). Robust facial landmark detection via a fully-convolutional local-global context network. IEEE Conference on Computer Vision and Pattern Recognition, 2018, 781–790. https://openaccess.thecvf.com/content_cvpr_2018/papers/Merget_Robust_Facial_Landmark_CVPR_2018_paper.pdf
Moore, D. R., Cheng, M. I., & Dainty, A. R. (2002). Competence, competency and competencies: performance assessment in organisations. Work Study, 51(6), 314–319. https://doi.org/10.1108/00438020210441876
Nikolaou, I., & Foti, K. (2018). Personnel selection and personality. In Virgil Zeigler-Hill, Todd K. Shackelford (Ed.), The SAGE handbook of personality and individual differences (pp. 458–474). SAGE
Nowicki, S., & Duke, M. P. (1994). Individual differences in the nonverbal communication of affect: The Diagnostic Analysis of Nonverbal Accuracy Scale. Journal of Nonverbal behavior, 18(1), 9–35. https://doi.org/10.1007/BF02169077
Nunnally, J. C. (1978). Psychometric theory (2nd Ed.). McGraw-Hill.
Orpen, C. (1985). The effects of long-range planning on small business performance: A further examination. Journal of Small Business Management (pre–1986), 23(000001), 16. https://www.proquest.com/docview/210754717?pq-origsite=gscholar&fromopenview=true
Pfister, T., Li, X., Zhao, G., & Pietikäinen, M. (2011, November). Recognising spontaneous facial micro-expressions. 2011 International Conference on Computer Vision, 2011, 1449–1456. https://doi.org/10.1109/ICCV.2011.6126401
Pitaloka, D. A., Wulandari, A., Basaruddin, T., & Liliana, D. Y. (2017). Enhancing CNN with preprocessing stage in automatic emotion recognition. Procedia Computer Science, 116, 523–529. https://doi.org/10.1016/j.procs.2017.10.038
Polikovsky, S., Kameda, Y., & Ohta, Y. (2009, December 3). Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor[Conference presentation]. 3rd International Conference on Imaging for Crime Detection and Prevention, London, UK. https://doi.org/10.1049/ic.2009.0244
Pursche, T., Clauß, R., Tibken, B., & Möller, R. (2019). Using neural networks to enhance the quality of ROIs for video based remote heart rate measurement from human faces. 2019 IEEE International Conference on Consumer Electronics, 2019, 1–5. https://doi.org/10.1109/ICCE.2019.8661915
Rao S. B, P., Rasipuram, S., Das, R., & Jayagopi, D. B. (2017, November 3). Automatic assessment of communication skill in non-conventional interview settings: a comparative study[Conference presentation]. 19th ACM International Conference on Multimodal Interaction, New York, USA. https://doi.org/10.1145/3136755.3136756
Reinhard, M. A., Scharmach, M., & Müller, P. (2013). It's not what you are, it's what you know: Experience, beliefs, and the detection of deception in employment interviews. Journal of Applied Social Psychology, 43(3), 467–479. https://doi.org/10.1111/j.1559-1816.2013.01011.x
Rosenberg, E. L., Ekman, P., Jiang, W., Babyak, M., Coleman, R. E., Hanson, M., O'Connor, C., Waugh, R., & Blumenthal, J. A. (2001). Linkages between facial expressions of anger and transient myocardial ischemia in men with coronary artery disease. Emotion, 1(2), 107–115. https://doi.org/10.1037/1528-3542.1.2.107
Rotondo, J. L. (2000). Dominance and gender in conversational interaction. University of Notre Dame.
Roulin, N., Bangerter, A., & Levashina, J. (2014). Interviewers' perceptions of impression management in employment interviews. Journal of Managerial Psychology, 29(2), 141–163. https://doi.org/10.1108/JMP-10-2012-0295
Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 23–38. https://doi.org/10.1109/34.655647
Russell, J. A., Bachorowski, J. A., & Fernández-Dols, J. M. (2003). Facial and vocal expressions of emotion. Annual Review of Psychology, 54(1), 329–349. https://doi.org/10.1146/annurev.psych.54.101601.145102
Sadeghi, H., & Raie, A. A. (2019). Human vision inspired feature extraction for facial expression recognition. Multimedia Tools & Applications, 78(21), 30335–30353. https://doi.org/10.1007/s11042-019-07863-z
Schmidt, Frank. (2016). The validity and utility of selection methods in personnel psychology: practical and theoretical implications of 100 years of research findings. Tippie College of Business, University of Iowa. https://www.researchgate.net/publication/309203898
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262–274. https://doi.org/10.1037/0033-2909.124.2.262
Shreve, M., Godavarthy, S., Goldgof, D., & Sarkar, S. (2011, March). Macro-and micro-expression spotting in long videos using spatio-temporal strain. 2011 IEEE International Conference on Automatic Face & Gesture Recognition, 2011, 51–56. https://doi.org/10.1109/FG.2011.5771451
Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. https://doi.org/10.1037/0033-2909.86.2.420
Sklansky, M. A., Isaacs, K. S., & Haggard, E. A. (1960). A method for the study of verbal interaction and levels of meaning in psychotherapy. In J. S. Gottlieb and G. Tourney (Ed.), Scientific papers and discussions, divisional meeting, Mid-West area distr branches (pp. 133–148). American Psychiatric Association
Spector, P. E., & Jex, S. M. (1998). Development of four self-report measures of job stressors and strain: interpersonal conflict at work scale, organizational constraints scale, quantitative workload inventory, and physical symptoms inventory. Journal of Occupational Health Psychology, 3(4), 356–367. https://psycnet.apa.org/buy/1998-12418-005
Staw, B. M., & Barsade, S. G. (1993). Affect and Managerial Performance: A Test of the Sadder-but-Wiser vs. Happier-and-Smarter Hypotheses. Administrative Science Quarterly, 38(2), 304–331. https://doi.org/10.2307/2393415
Suen, H. Y., Chen, M. Y. C., & Lu, S. H. (2019). Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes?. Computers in Human Behavior, 98, 93–101. https://doi.org/10.1016/j.chb.2019.04.012
Suen, H. Y., Hung, K. E., & Lin, C. L. (2020). Intelligent video interview agent used to predict communication skill and perceived personality traits. Human-centric Computing and Information Sciences, 10(1), 1–12. https://doi.org/10.1186/s13673-020-0208-3
Sung, K. K., & Poggio, T. (1998). Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 39–51. https://doi.org/10.1109/34.655648
Takalkar, M., Xu, M., Wu, Q., & Chaczko, Z. (2018). A survey: facial micro-expression recognition. Multimedia Tools and Applications, 77(15), 19301–19325. https://doi.org/10.1007/s11042-017-5317-2
Taylor, P. (1999). Hackers: Crime and the digital sublime. Routledge.
Varma, A., Denisi, A. S., & Peters, L. H. (1996). Interpersonal affect and performance appraisal: A field study. Personnel Psychology, 49(2), 341–360. https://doi.org/10.1111/j.1744-6570.1996.tb01803.x
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009). Social signal processing: survey of an emerging domain. Image and Vision Computing, 27(12), 1743–1759. https://doi.org/10.1016/j.imavis.2008.11.007
Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
Waller, B. M., Whitehouse, J., & Micheletta, J. (2017). Rethinking primate facial expression: A predictive framework. Neuroscience & Biobehavioral Reviews, 82, 13–21. https://doi.org/10.1016/j.neubiorev.2016.09.005
Walther, J. B. (2011). Theories of computer-mediated communication and interpersonal relations. In M.L. Knapp, J.A. Daly (Ed.), The Handbook of Interpersonal Communication(4th ed., pp.443–479). SAGE.
Wang, P., & Ji, Q. (2004). Multi-View Face Detection under Complex Scene based on Combined SVMs. Proceedings of the 17th International Conference on Pattern Recognition, 4, 179–182. https://www.ecse.rpi.edu/~cvrl/FaceProject/Homepage/Publication/ICPR04_final_cameraready_v4.pdf
Wang, S. J., Chen, H. L., Yan, W. J., Chen, Y. H., & Fu, X. (2014). Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine. Neural Processing Letters, 39(1), 25–43. https://doi.org/10.1007/s11063-013-9288-7
Wang, Y., See, J., Phan, R. C. W., & Oh, Y. H. (2014). LBP with six intersection points: Reducing redundant information in lbp-top for micro-expression recognition. In Cremers D., Reid I., Saito H., Yang MH. (Ed.), Computer Vision – ACCV 2014(pp. 525–537). Springer, Cham.
Yamashita, R., Nishio, M., Do, R.K.G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights Imaging, 9, 611–629. https://doi.org/10.1007/s13244-018-0639-9
Yang, S., & Bhanu, B. (2012). Understanding discrete facial expressions in video using an emotion avatar image. IEEE Transactions on Systems, Man, and Cybernetic, 42(4), 980–992. https://doi.org/10.1109/TSMCB.2012.2192269
Yeasin, M., Bullot, B., & Sharma, R. (2006). Recognition of facial expressions and measurement of levels of interest from video. IEEE Transactions on Multimedia, 8(3), 500–508. https://doi.org/ 10.1109/TMM.2006.870737
Young, A. W., Perrett, D., Calder, A., Sprengelmeyer, R., & Ekman, P. (2002). Facial expressions of emotion: Stimuli and tests (FEEST).
Yudin, D. A., Dolzhenko, A. V., & Kapustina, E. O. (2019, October). The usage of grayscale or color images for facial expression recognition with deep neural networks. In Kryzhanovsky B., Dunin-Barkowski W., Redko V., Tiumentsev Y. (Ed.), Advances in neural computation, Machine Learning, and Cognitive Research III: Selected Papers from the XXI International Conference on Neuroinformatics (pp. 271–281). Springer, Cham.
Zeng, Z., Gong, Q., & Zhang, J. (2019, March). CNN model design of gesture recognition based on tensorflow framework. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, 2019, 1062–1067. https://doi.org/10.1109/ITNEC.2019.8729185