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.
Highlights
Gender identification plays a remarkable role in social communication
We show that these features provide better gender discriminating results than the current state-of-the-art methods, which is due to the improved capability of the included features to represent the shape of 3D facial surfaces
We found that linear discriminant analysis (LDA) outperformed another popular classifier of choice, the support vector machine (SVM) [60]
Summary
Gender identification plays a remarkable role in social communication. They are remarkably accurate at determining the gender of subjects from their facial appearance. Achieving similar accuracy in automatic gender classification using computers remains a challenge. It is crucial in many applications, for instance making human– computer interaction (HCI) more user friendly, conducting passive surveillance and access control, and collecting valuable statistics, such as the number of women who enter a store on a given day. Researchers have considered techniques for gender classification since the 1990s, when SexNet, the first automated system capable of gender recognition using the human face, was created [8]
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