Novel descriptors for geometrical 3D face analysis

Abstract

3D face was recently investigated for various applications, including biometrics and diagnosis. Describing facial surface, i.e. how it bends and which kinds of patches is composed by, is the aim of studies of Face Analysis, whose ultimate goal is to identify which features could be extracted from three-dimensional faces depending on the application. In this study, we propose 105 novel geometrical descriptors for Face Analysis. They are generated by composing primary geometrical descriptors such as mean, Gaussian, principal curvatures, shape index, curvedness, and the coefficients of the fundamental forms, and by applying standard functions such as sine, cosine, and logarithm to them. The new descriptors were mapped on 217 facial depth maps and analysed in terms of descriptiveness of facial shape and exploitability for localizing landmark points. Automatic landmark extraction stands as the final aim of this analysis. Results showed that some newly generated descriptors were sounder than the primary ones, meaning that their local behaviours in correspondence to a landmark position is thoroughly specific and can be registered with high similarity on every face of our dataset.

A Novel Framework for Assessing Facial Attractiveness Based on Facial Proportions

“…Vezzetti et al [31] introduced an automatic landmark extraction method on a 3D face. Marcolin et al [32] also devised facial geometrical descriptors representing symmetry features for the attractiveness analysis of 3D faces. Liao et al [33] manually selected landmarks for the golden ratio, neoclassical canon, and symmetry on a 3D face, and these distances and ratio values were revised by considering the surface characteristics of a 3D face.…”

Augmented Reality: Advances in Diagnostic Imaging

“…Future applications of AR/VR with D3D may include complex imaging such as the connections of the brain as in diffusion tensor imaging [33,34], dual positron emission tomography (PET)-MRI [35] or even 3D facial recognition, analysis or cosmesis [36][37][38].…”

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“…Another example of heterogeneous face recognition is sketch-photo matching [4], which is often used to detect suspects in forensic cases. In addition to 2D data, with the easy access to sensors used to acquire 3D data in recent years, 3D face images have become widely used for facial recognition [5] and facial analysis [6] for different purposes. In this work, we propose a face recognition scheme for 2D still images in visible and infrared spectra.…”