Perceptual Differences between Men and Women: A 3D Facial Morphometric Perspective

Understanding the features employed by the human visual system in gender classification is considered a critical step towards improving machine based gender classification systems. We propose the use of 3D Euclidean and geodesic distances between biologically significant facial landmarks to classify gender. We perform five different experiments on the BU-3DFE face database to look for more representative features that can replicate our visual system. Based on our experiments we suggest that the human visual system looks at the ratio of 3D Euclidean and geodesic distance as these features can classify facial gender with an accuracy of 99.32%. The features selected by our proposed gender classification experiment are robust to ethnicity and moderate changes in expression. They also replicate the perceptual gender bias towards certain features and hence become good candidates for being a more representative feature set.