3D Facial Similarity Measure Based on Geodesic Network and Curvatures

Automated 3D facial similarity measure is a challenging and valuable research topic in anthropology and computer graphics. It is widely used in various fields, such as criminal investigation, kinship confirmation, and face recognition. This paper proposes a 3D facial similarity measure method based on a combination of geodesic and curvature features. Firstly, a geodesic network is generated for each face with geodesics and iso-geodesics determined and these network points are adopted as the correspondence across face models. Then, four metrics associated with curvatures, that is, the mean curvature, Gaussian curvature, shape index, and curvedness, are computed for each network point by using a weighted average of its neighborhood points. Finally, correlation coefficients according to these metrics are computed, respectively, as the similarity measures between two 3D face models. Experiments of different persons’ 3D facial models and different 3D facial models of the same person are implemented and compared with a subjective face similarity study. The results show that the geodesic network plays an important role in 3D facial similarity measure. The similarity measure defined by shape index is consistent with human’s subjective evaluation basically, and it can measure the 3D face similarity more objectively than the other indices

Description-based visualisation of ethnic facial types

This study reports on the design and evaluation of a tool to assist in the description and visualisation of the human face and variations in facial shape and proportions characteristic of different ethnicities. A comprehensive set of local shape features (sulci, folds, prominences, slopes, fossae, etc.) which constitute a visually-discernible ‘vocabulary’ for facial description. Each such feature has one or more continuous-valued attributes, some of which are dimensional and correspond directly to conventional anthropometric distance measurements between facial landmarks, while other attributes capture the shape or topography of that given feature. These attributes, distributed over six facial regions (eyes, nose, etc.), control a morphable model of facial shape that can approximate individual faces as well as the averaged faces of various ethnotypes. Clues to ethnic origin are often more effectively conveyed by shape attributes than through differences in anthropometric measurements due to large individual differences in facial dimensions within each ethnicity. Individual faces of representative ethnicities (European, East Asian, etc.) can then be modelled to establish the range of variation of the attributes (each represented by a corresponding three-dimensional ‘basis shape’). These attributes are designed to be quasi-orthogonal, in that the model can assume attribute values in arbitrary combination with minimal undesired interaction. They thus can serve as the basis of a set of dimensions or degrees of freedom. The space of variation in facial shape defines an ethnicity face space (EFS), suitable for the human appreciation of facial variation across ethnicities, in contrast to a conventional identity face space (IFS) intended for automated detection of individual faces out of a sample set of faces from a single, homogeneous population. The dimensions comprising an IFS are based on holistic measurements and are usually not interpretable in terms of local facial dimensions or shape (i.e., they are not ‘semantic’). In contrast, for an EFS to facilitate our understanding of ethnic variation across faces (as opposed to ethnicity recognition) the underlying dimensions should correspond to visibly-discernible attributes. A shift from quantitative landmark-based anthropometric comparisons to local shape comparisons is demonstrated. Ethnic variation can be visually appreciated by observing the changes in a model through animation. These changes can be tracked at different levels of complexity: across the whole face, by selected facial region, by isolated feature, and by isolated attribute of a given feature. This study demonstrates that an intuitive feature set, derived by artistically-informed visual observation, can provide a workable descriptive basis. While neither mathematically-complete nor strictly orthogonal, the feature space permits close surface fits between the morphable model and face scan data. This study is intended for the human visual appreciation of facial shape, the characteristics of differing ethnicities, and the quantification of those differences. It presumes a basic understanding of the standard practices in digital facial animation

On constructing facial similarity maps

Automatically determining facial similarity is a difficult and open question in computer vision. The problem is complicated both because it is unclear what facial features humans use to determine facial similarity and because facial similarity is subjective in nature: similarity judgements change from person to person. In this work we suggest a system which places facial similarity on a solid computational footing. First we describe methods for acquiring facial similarity ratings from humans in an efficient manner. Next we show how to create feature vector representations for each face by extracted patches around facial keypoints. Finally we show how to use the acquired similarity ratings to learn functional mapping which project facialfeature vectors into Face Spaces which correspond to our notions of facial similarity. We use different collections of images to both create and validate the Face Spaces including: perceptual similarity data obtained from humans, morphed faces between two different individuals, and the CMU PIE collection which contains images of the same individual under different lighting conditions. We demonstrate that using our methods we can effectively create Face Spaces which correspond to human notions of facial similarity. 1