We evaluated the proposed template morphing algorithms using both the BU3D dataset [149] and the Headspace dataset [1]. Section 4.6.1 presents the validation of the tem- plate adaptation approach. In this thesis, we proposed three registration methods: (1) the method detailed in this chapter, (2) the symmetric deformation algorithm detailed in chap- ter 4, and (3) our earlier method described in Appendix B and [47]. These are compared in section 4.6.2. In section 4.6.3, we present the qualitative and quantitative evaluation of the correspondence establishment with other competing template morphing approaches.
4.6.1
Validation of Template Adaptation
In order to validate the effectiveness of each key step in the proposed registration pipeline, we first remove the process of template adaption from the pipeline and evaluate perfor- mance. We then replace this and remove LB regularised projection from the proposed framework and again evaluate performance qualitatively and quantitatively, comparing both modified pipeline cases with the full pipeline. Typical results for a child in the Headspace dataset are shown in Figure 4.11. After pure rigid alignment without template adaptation, the nose of the template is still bigger than the target. As can be seen in Figure 4.11 (3), the nose ends up with a bad deformation result. The same problem happened in the ear. Without LB regularised projection, shown in Figure 4.11 (4), it fails in cap- turing the shape detail compared with the proposed full pipeline. The adaptive template improves the correspondence accuracy in local regions, while the LB regularised point projection helps in decreasing the correspondence error in the normal direction.
Using the BU3D dataset for quantitative validation, we compared the performance of (i) the proposed ICPD registration, (ii) ICPD with an adaptive template using LB mesh
4.6 Evaluation of Correspondences
(1)Target scan (2) proposed
(3) no template adaptation (4) no LB regularised projection
Figure 4.11: Internal comparison of approaches: (1) target scan; (2) proposed full pipeline method; (3) template adaptation removed; (4) LB regularised projection removed. Error map shown in mm.
Table. Convergence of ICPD
ICPD LB PM ICP loops 6.47 3.52 3.74
Time (s) 831.35 426.13 434.53
Figure 4.12: Improvement in correspondence and convergence performance when using adaptive templates: 1) ICPD without an adaptive template (cyan); 2) ICPD with LB-based adaptive template (blue); 3) ICPD with adaptive PM-based template (blue dashed).
manipulation and (iii) ICPD with an adaptive template, using a posterior model (PM). The mean per-vertex error is computed between the registration results and their ground-truth. The number of ICPD iterations and computation time is recorded, when using the same computation platform. The per-vertex error plot in Figure 4.12 illustrates that the adaptive template improves the correspondence accuracy of ICPD. The number of ICPD iterations
and computation time is significantly decreased by the adaptive template method. In par- ticular adaptive template using LB mesh manipulation has better performance than adap- tive template using a posterior model. Thus, we employ an adaptive template approach using LB mesh manipulation for later experiments.
4.6.2
Comparison Between Proposed Approaches
During this thesis work, we proposed three different template morphing algorithms, as discussed earlier. We believe that the template morphing framework in this chapter is our current best system. To validate this, we compare our proposed template morph- ing algorithms: ICPD with adaptive template [46] presented in this chapter, hierarchical parts-basedCPD-LB [47] (also in Appendix B) and symmetry-aware CPD [48] (also in Chapter 4). Figure 4.13 illustrates that the eye region and mouth region can have a small over-fitting problem in morphing when using either hierarchical parts-based CPD-LB or symmetry-aware CPD. The third row in Figure 4.13 shows that ICPD with adaptive template gives a better morphing in ear region where outliers exists in the raw data.
To evaluate the three approaches quantitatively, we use 14 manual facial landmarks over 100 subjects to measure landmark error of each template deformation method. The mean per-vertex error is calculated by the mean nearest point error between the morphed template and its corresponding 3D mesh. As can be seen from Figure 4.14 (1), ICPD with adaptive template is much better than the other two in terms of mean per-vertex error. Fig- ure 4.14 (2) shows that ICPD with adaptive template achieves slightly better performance in the metric of landmark error. The technique detail of hierarchical parts-based CPD-LB is included in Appendix A. We introduce the symmetry-aware CPD in Chapter 4, which shows that symmetry-aware CPD achieves better performance in craniofacial symmetry preservation. These comparison results imply that the method described in this chapter is the best correspondence establishment framework. It may be possible to integrate the work in this chapter with our symmetry work in Chapter 4, but this was not attempted.
4.6 Evaluation of Correspondences
(1) Raw data (2) proposed (3) symmetryCPD (4) CPD-LB
Figure 4.13: Morphing results: (1) raw mesh; (2) ICPD with adaptive template (this chapter); (3) symmetry-aware CPD (Chapter 4); (4) hierarchical parts-based CPD-LB (Appendix B).
4.6.3
Comparison with Existing Literature
In this section, we compare the proposed method with two publicly-available registra- tion frameworks, namely the Large-Scale Face Model (LSFM) pipeline [27] and Open Framework (OF) [67], using the Headspace dataset.
Qualitative Evaluation: Figure 4.15 shows a typical example where the proposed method is qualitatively superior to other methods with respect to shape detail capture, and the accuracy of the mouth region. The LSFM pipeline captures shape detail but the
(1) (2)
Figure 4.14: (1) Mean per-vertex nearest point error; (2) Landmark error.
Scan
(1) (2) (3) (4)
Figure 4.15: First row - correspondence results and their landmarks compared with ground-truth on raw scan; Second row - the color map of per-vertex nearest point error. (1) proposed method with LB template adaptation; (2) proposed method without adaptive template; (3) Open Framework morphing [94]; (4) LSFM morphing [26].
mouth region is not close to the raw scan. The OF pipeline has a smooth deformation field, thereby failing to capture shape detail. The OF approach requires a point projection stage after the shape registration to eliminate the shape normal error.
Quantitative Evaluation: Figure 4.16-left shows cumulative histograms for shape error, where shape error is computed by measuring the nearest point distance from the de-