A Minimax Framework for Gender Classification Based on Small-Sized Datasets

Gender recognition is a topic of high interest especially in the growing field of audience measurement techniques for digital signage applications. Usually, supervised approaches are employed and they require a preliminary training phase performed on large datasets of annotated facial images that are expensive (e.g. MORPH) and, anyhow, they cannot be updated to keep track of the continuous mutation of persons' appearance due to changes of fashions and styles (e.g. hairstyles or makeup). The use of small-sized (and then updatable in a easier way) datasets is thus high desirable but, unfortunately, when few examples are used for training, the gender recognition performances dramatically decrease since the state-of-art classifiers are unable to handle, in a reliable way, the inherent data uncertainty by explicitly modeling encountered distortions. To face this drawback, in this work an innovative classification scheme for gender recognition has been introduced: its core is the Minimax approach, i.e. a smart classification framework that, including a number of existing regularized regression models, allows a robust classification even when few examples are used for training. This has been experimentally proved by comparing the proposed classification scheme with state of the art classifiers (SVM, kNN and Random Forests) under various pre-processing methods.