Automated Screening of Job Candidate Based on Multimodal Video Processing

The selection of adequate job candidates is very long and challenging process for each employer. The system presented in this paper is aiming to decrease the time for candidate selection on the pre-employment stage using automatic personality screening based on visual, audio and lexical cues from short video-clips. The system is build to predict candidate scores of 5 Big Personality Traits and to estimate a final decision, to which degree the person from video-clip has to be invited to the job interview. For each channel a set of relevant features is extracted, which are used to train separately from each other using Deep Learning. In the final stage all three results are fused together into final scores prediction. The experiment was conducted on first impression database and achieved significant performance.

[1]  Qiang Zhang,et al.  An Efficient Method of Key-Frame Extraction Based on a Cluster Algorithm , 2013, Journal of human kinetics.

[2]  Daniel Gatica-Perez,et al.  You Are Known by How You Vlog: Personality Impressions and Nonverbal Behavior in YouTube , 2011, ICWSM.

[3]  Mohd Heikal Husin,et al.  Sentiment Valences for Automatic Personality Detection of Online Social Networks Users Using Three Factor Model , 2015 .

[4]  Sergio Escalera,et al.  Fusion of classifier predictions for audio-visual emotion recognition , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[5]  A. Rogier [Communication without words]. , 1971, Tijdschrift voor ziekenverpleging.

[6]  P. Jackson,et al.  Multimodal Emotion Recognition , 2010 .

[7]  Tim Polzehl,et al.  Automatically Assessing Personality from Speech , 2010, 2010 IEEE Fourth International Conference on Semantic Computing.

[8]  Alessandro Vinciarelli,et al.  A Survey of Personality Computing , 2014, IEEE Transactions on Affective Computing.

[9]  Gregory J. Park,et al.  Automatic personality assessment through social media language. , 2015, Journal of personality and social psychology.

[10]  Shogo Muramatsu,et al.  Video key frame selection by clustering wavelet coefficients , 2004, 2004 12th European Signal Processing Conference.

[11]  Gholamreza Anbarjafari,et al.  Efficiency of chosen speech descriptors in relation to emotion recognition , 2017, EURASIP Journal on Audio, Speech, and Music Processing.

[12]  Greg Hamerly,et al.  Accelerating Lloyd’s Algorithm for k -Means Clustering , 2015 .

[13]  Sergio Escalera,et al.  Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits , 2016, ECCV Workshops.

[14]  Alessandro Vinciarelli,et al.  The voice of personality: mapping nonverbal vocal behavior into trait attributions , 2010, SSPW '10.

[15]  Marco Cristani,et al.  Social profiling through image understanding: Personality inference using convolutional neural networks , 2017, Comput. Vis. Image Underst..

[16]  Subramanian Ramanathan,et al.  Automatic modeling of personality states in small group interactions , 2011, MM '11.

[17]  Josva Kleist,et al.  IEEE Fourth International Conference on eScience, 2008. eScience '08 , 2008 .

[18]  S. Srivastava,et al.  The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. , 1999 .

[19]  Li Lin,et al.  Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering , 2004, IEEE Transactions on Engineering Management.

[20]  Daniel Gatica-Perez,et al.  The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs , 2013, IEEE Transactions on Multimedia.