In this work, we use cognitive modeling to estimate the ”gender strength” of frontal faces, a continuous class variable, superseding the traditional binary class labeling. To incorporate this continuous variable we suggest a novel linear gender classification algorithm, the Gender Strength Regression. In addition, we use the gender strength to construct a smaller but refined training set, by identifying and removing ill-defined training examples. We use this refined training set to improve the performance of known classification algorithms. Also the human performance of known data sets is reported, and surprisingly it seems to be quite a hard task for humans. Finally our results are reproduced on a data set of above 40,000 public Danish LinkedIN profile pictures.
Lecture Notes in Computer Science: Workshops and Demonstrations, Part II, 2012, p. 300-308
Gender recognition; Linear Discriminant Analysis; Support Vector Machines; Cognitive Modeling; Linear Regression
Main Research Area:
Lecture Notes in Computer Science
12th European Conference on Computer Vision (ECCV 2012)European Conference on Computer Vision