The surge in video recordings

The current surge in video recordings of professional interactions (e.g. video CV’s, webinars, video conferences) represents a true opportunity to develop automated systems for behaviour-based inference of traits and states as explained above, by leveraging fast, and scalable algorithms built upon ground truth data that is both large and high in quality. The development of such systems, including Vima’s, typically has three steps.

3 steps

  1. Human raters or annotators evaluate video recordings of expressions (e.g. video CVs) on key questions relating to particular skills and personality traits (e.g. “Is the person willing, eager to talk?”). At Vima, human annotators are trained, expert observers who meet the statistical standards of high interrater agreement.
  2. In parallel, the recorded expressions are analyzed by some behaviour feature extraction tools taken from libraries of independently developed algorithms.
  3. Steps (1) and (2) are integrated into the training of a machine learning model that is aimed to predict the human evaluations based only on the extracted behaviour features. The final test of the system is performed on video material that was not included in the training phase (cross-validation). If the prediction model is accurate, it means that this type of human evaluation of skills and personality traits are reliably and systematically correlated with the extracted behaviour features and that the model can be applied to any other new set of video recordings showing a similar type of expressions (e.g. video CVs).

Accuracy and more information

The accuracy of the model can be compared to a minimum of 6 experts in psychology assessing a person on personality traits, soft skills and distress.

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