Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images
Joseph Lemley, Sami Abdul-Wahid, Dipayan Banik, Răzvan Andonie · MAICS · 2016
Recently, several machine learning methods for gender classi- fication from frontal facial images have been proposed. Their variety suggests that there is not a unique or generic solution to this problem. In addition to the diversity of methods, there is also a diversity of benchmarks used to assess them. This gave us the motivation for our work: to select and compare in a concise but reliable way the main state-of-the-art methods used in automatic gender recognition. As expected, there is no overall winner. The winner, based on the accuracy of the classification, depends on the type of benchmarks used.