PLS-SEM technique was used to test the research model. The data was collected from IT executives working in Malaysian Software companies (n = 396). The purpose of the study is to examine the direct effect of interpersonal conflict on turnover intention and indirect effect of psychological well-being between interpersonal conflict and turnover intention. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point’s location and increase the accuracy and robustness of the algorithm.
Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem.
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