A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features

Abstract

The relationship between the shape and gender of a face, with particular application to automatic gender classification, has been the subject of significant research in recent years. Determining the gender of a face, especially when dealing with unseen examples, presents a major challenge. This is especially true for certain age groups, such as teenagers, due to their rapid development at this phase of life. This study proposes a new set of facial morphological descriptors, based on 3D geodesic path curvatures, and uses them for gender analysis. Their goal is to discern key facial areas related to gender, specifically suited to the task of gender classification. These new curvature-based features are extracted along the geodesic path between two biological landmarks located in key facial areas.Classification performance based on the new features is compared with that achieved using the Euclidean and geodesic distance measures traditionally used in gender analysis and classification. Five different experiments were conducted on a large teenage face database (4745 faces from the Avon Longitudinal Study of Parents and Children) to investigate and justify the use of the proposed curvature features. Our experiments show that the combination of the new features with geodesic distances provides a classification accuracy of 89%. They also show that nose-related traits provide the most discriminative facial feature for gender classification, with the most discriminative features lying along the 3D face profile curve.

Facial Genetics: A Brief Overview

“…These landmarks are defined by identifiable/describable facial features, e.g., nasion, inner/outer canthi, commissures that can generate Euclidean distances, angles, and ratios ( Farkas et al, 2002 , 2004 , 2005 ). One or more facial landmarks can be used to generate principal components, geodesic distances, geodesic arrays, facial shells and signatures which can categorize patterns in facial features ( Hammond and Suttie, 2012 ; Hallgrimsson et al, 2015 ; Tsagkrasoulis et al, 2017 ; Abbas et al, 2018 ). In addition, anthropometric masks have been proposed whereby five landmarks are used to crudely orientate the 3D facial shells which are then non-rigidly mapped on to a template which generates about 10,000 quasi landmarks ( Claes et al, 2012 ).…”

“…Face height (28.8%), width of the eyes (10.4%) and prominence of the nose (6.7%) explained 46% of total facial variance ( Toma et al, 2012 ). There were subtle differences between males and females in relation to the relative prominence of the lips, eyes, and nasal bridges including minor facial asymmetries ( Toma et al, 2008 , 2012 ; Wilson et al, 2013 ; Abbas et al, 2018 ). The dimorphic differences appear to follow similar patterns in different ethnic groups ( Farnell et al, 2017 ).…”

Real-time facial pose estimation and tracking by coarse-to-fine iterative optimization

“…Cao et al [15] achieved inherent mapping between frontal and profile faces, whereas many existing works may only focus on frontal faces, thus enhancing the face recognition performance. Abbas et al [16] proposed a method for facial morphological descriptors based on 3D geodesic path curvatures. However, this method was still applied only to more salient features, such as gender analysis.…”

Biological landmark Vs quasi-landmarks for 3D face recognition and gender classification

“…The clear sexual dimorphism of the human face makes face gender classification considerably efficient and fast operation [7]. In spite of, the abundant gender classification research, the task of developing easy to implement, automated and robust face gender classification is still a remote objective.…”

“…Many present works on gender classification have used 2-D face images for solving gender classification problem, but 3-D face surface has not so far been used widely for gender classification. Diverse psychology encourages to use 3-D shape information for gender classification [7,8].…”