@article{PB-VRVis-2018-040,
author = {Bešenic, Krešimir and Matkovi{\'c}, Kresimir},
title = {Automatic Image-based Face Analysis Systems Overview},
year = {2018},
journaltitle = {Engineering power : bulletin of the Croatian Academy of
  Engineering (1331-7210)},
url = {https://www.vrvis.at/publications/PB-VRVis-2018-040},
pages = {2-7},
abstract = {Face analysis systems have recently gained popularity due to  the large number of potential applications  across a wide range of industries. Various types of information  can be extracted from an image of a face including: face  location and size, location of characteristic facial landmark  points, 3D head pose, facial expression and emotion, gaze  direction and biometric information (i.e. age, gender and race).  Most of these problems are solved using machine learning  techniques based on large sets of training samples. Furthermore,  information from these different tasks is often complementary  and can be used to enhance the accuracy of the algorithms. A  systematic overview of current approaches to face analysis tasks  is presented as an introduction to this growing  research field.},
number = {13},
}