Another study that assessed anthropometric measurements of the Saudi Arabian nose was conducted by Al-Qattan et al. [24], who reported a higher ICD and lower NW in both sexes and a shorter middle third of the face in women in comparison to our study. However, due to differences in measurement technique, with the previous study using the indirect anthropometry method of photogrammetry, the results of the two studies cannot be directly compared.
Face anthropometry provides a set of meaningful measurements or shape parameters that allow the most complete control over the shape of the face. Farkas [5] describes a widely used set of measurements to characterize the human face. The measurements are taken between the landmark points defined in terms of visually-identifiable or palpable features on the subject face using carefully specified procedures and measuring instruments. Such measurements use a total of 47 landmark points for describing the face. As described in Section 2, each example in our face scan database is equipped with 86 landmarks. Following the conventions laid out in [5], we have chosen a subset of 38 landmarks for anthropometric measurements (see Figure 6).
Farkas [5] describes a total of 132 measurements on the face and head. Instead of supporting all 132 measurements, we are only concerned with those related to five facial features (including global face outline). In this paper, 68 anthropometric measurements are chosen as shape control parameters. As an example, Table 1 lists the nasal measurements used in our work. The example models are placed in the standard posture for anthropometric measurements. In particular, the axial distances correspond to the ?, ?, and ? axes of the world coordinate system. Such a systematic collection of anthropometric measurements is taken through all example models in the database to determine their locations in a multi-dimensional measurement space.
Our method has been implemented in an interactive system with C++/OpenGL, where the user can select facial features to work on interactively. A GUI snapshot is shown in Figure 9. Our system starts with a mean model which is computed as the average of 186 meshes of the RBF-warped models and textured with the mean cylindrical full-head texture image [38]. Our system also allows the user to select the desired feature(s) from a database of pre-constructed typical features, which are shown in the small icons on the upper-left of the GUI. Upon selecting a feature from the database, the feature will be imported seamlessly into the displayed head model and can be further edited if needed. The slider positions for each of the available feature in the database are stored by the system so that their configuration can be restored whenever the feature is chosen. Such a feature importing mode enables coarse-to-fine modification of features, making the face synthesis process less tedious. We invited several student users who naturally lack the graphics professional's modeling background to create face models using our system. The laymen appreciated the intuitiveness and continuous variability of the control sliders. Table 2 shows the details of the datasets.
Figure 10 illustrates a number of distinct facial shapes synthesized to satisfy user-specified local shape constraints. Clear differences are found in the width of the nose alar wings, the straightness of the nose bridge, the inclination of the nose tip, the roundness of eyes, the distance between eyebrows and eyes, the thickness of mouth lips, the shape of the lip line, the sharpness of the chin, and so forth. A morphing can be generated by varying the shape parameters continuously, as shown in Figures 10(b) and 10(c). In addition to starting with the mean model, the user may also select the desired head model of a specific person from the example database for further editing. Figure 11 illustrates face editing results on the models of two individuals for various user-intended characteristics.
In order to quantify the performance, we arbitrarily selected ten examples in the database for the cross validation. Each example has been excluded from the example database in training the face synthesis system and its shape measurements were used as a test input to the system. The output model was then compared against the original model. Figure 12 shows a visual comparison of the result. We assess the reconstruction by measuring the maximum, mean, and root mean square (RMS) errors from the feature regions of the output model to those of the input model. The 3D errors are computed by the Euclidean distance between each vertex of the ground truth and synthesized model. Table 3 shows the average errors measured for the ten reconstructed models. The errors are given using both absolute measures (/mm) and as a percentage of the diameter of the output head model bounding box.
We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.
His interest in craniofacial morphology began during his period in Prague. Early in his surgical career, he became dissatisfied with the determination of the morphological changes in the head and face using visual assessment alone. Thus, he began to explore the use of classical anthropometric methods for the quantitative analysis of faces pre- and post-operatively. He collaborated with anthropologist Prof Karel Hajnis to develop an empirical system of facial measurements to analyze the faces of patients with congenital anomalies or facial deformities due to trauma.
Easily identifiable, reliable reference points were chosen for the comparative measurements (Fig. 1).8-10 At the time of the scan, direct anthropometric facial measurements were made directly on the patient's face with a sliding caliper (Table 1). A 2D photograph was also taken of the patient in natural head position, using a digital single-lens reflex camera (Nikon D3100*** with 18-55mm lens) at a standardized distance of 3m.11
Direct anthropometry is considered the gold standard for in vivo soft-tissue assessment; it is simple and relatively inexpensive, and it does not require complex instrumentation.12 Various methods have been proposed to obtain equally accurate 3D facial models, including 3D laser scanning, video imaging, 3D stereo cameras, digital color portraits, and volume wrapping. Of these, volume wrapping is the least time-consuming and easiest for the clinician, since most of the labor is performed by the imaging company. The technique is now used routinely by orthodontists and oral surgeons to simulate surgeries, but has not previously been evaluated in comparison with direct measurements of the face.
Anthropometric measurement of the face (88. Farkas LG. Examination. In: Farkas LG (editor). Anthropometry of the head and face. 2a ed. New York: Raven Press, 1994. P.3-56.,99. Farkas LG, Deutsch CK. Anthropometric determination of craniofacial morphology. Am J Med Genet. 1996;65(1):1-4.);
Anthropometry is the biological science of measuring the size, weight and proportion of the human body (88. Farkas LG. Examination. In: Farkas LG (editor). Anthropometry of the head and face. 2a ed. New York: Raven Press, 1994. P.3-56.) and provides objective data regarding craniofacial morphology through a series of head and face measurements (1010. Ward RE, Jamison PL, Farkas LG. Craniofacial variability index: a simple measure of normal and abnormal variation in the head and face. Am J Med Genet. 1998;80(3):232-40.). For the present study, we selected the following static measurements (Figure 3: The images presented are from one of the participants): (a) medial portion of the face - the diagonal distance from the external corner of the eye to the commissure (mouth); (b) lateral portion of the face - the diagonal distance from the tragus (ear) to the end point of the nasolabial fold projection on the mandible (i.e., measurements a and b estimate the volumes of the buccinator and masseter muscles, respectively); (c) masseter muscles- the horizontal distance from the center of the left masseter to the center of the right masseter (the muscle center point is estimated by asking the patient to clench their teeth to produce muscle contraction and determined by palpation of the region with maximal volume); (d) buccinators muscles - the horizontal distance from the center of the left buccinator to the center of the right buccinator (muscle center point is determined by measuring the midpoint between the external corner of the eye and the commissure).
b2a8ae9291