Understanding Critical Factors in Appearance-Based Gender Categorization

Gender categorization, based on the analysis of facial appearance, can be useful in a large set of applications. In this paper we investigate the gender classification problem from a non-conventional perspective. In particular, the analysis will aim to determine the factors critically affecting the accuracy of available technologies, better explaining differences between face-based identification and gender categorization. A novel challenging protocol is proposed, exploiting the dimensions of the Face Recognition Grand Challenge version 2.0 database (FRGC2.0). This protocol is evaluated against several classification algorithms and different kind of features, such as Gabor and LBP. The results obtained show that gender classification can be made independent from other appearance-based factors such as the skin color, facial expression, and illumination condition.