Zhao Junli, Duan Fuqing, Pan Zhenkuan, Wu Zhongke, Li Jinhua, Deng Qingqiong, Li Xiaona, Zhou Mingquan
School of Data Science and Software Engineering, Qingdao University, Qingdao, China.
College of Automation and Electrical Engineering, Qingdao University, Qingdao, China.
PLoS One. 2017 Jun 22;12(6):e0179671. doi: 10.1371/journal.pone.0179671. eCollection 2017.
The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery. The evaluation of craniofacial reconstruction results is important for improving the effect of craniofacial reconstruction. Here, we used the sparse principal component analysis (SPCA) method to evaluate the similarity between two sets of craniofacial data. Compared with principal component analysis (PCA), SPCA can effectively reduce the dimensionality and simultaneously produce sparse principal components with sparse loadings, thus making it easy to explain the results. The experimental results indicated that the evaluation results of PCA and SPCA are consistent to a large extent. To compare the inconsistent results, we performed a subjective test, which indicated that the result of SPCA is superior to that of PCA. Most importantly, SPCA can not only compare the similarity of two craniofacial datasets but also locate regions of high similarity, which is important for improving the craniofacial reconstruction effect. In addition, the areas or features that are important for craniofacial similarity measurements can be determined from a large amount of data. We conclude that the craniofacial contour is the most important factor in craniofacial similarity evaluation. This conclusion is consistent with the conclusions of psychological experiments on face recognition and our subjective test. The results may provide important guidance for three- or two-dimensional face similarity evaluation, analysis and face recognition.
计算机辅助颅面重建（CFR）技术已广泛应用于刑事侦查、考古学、人类学和整容外科等领域。评估颅面重建结果对于提高颅面重建效果具有重要意义。在此，我们使用稀疏主成分分析（SPCA）方法来评估两组颅面数据之间的相似性。与主成分分析（PCA）相比，SPCA能够有效降低维度，同时产生具有稀疏载荷的稀疏主成分，从而便于解释结果。实验结果表明，PCA和SPCA的评估结果在很大程度上是一致的。为了比较不一致的结果，我们进行了一项主观测试，结果表明SPCA的结果优于PCA。最重要的是，SPCA不仅可以比较两个颅面数据集的相似性，还可以定位高度相似的区域，这对于提高颅面重建效果很重要。此外，可以从大量数据中确定对颅面相似性测量重要的区域或特征。我们得出结论，颅面轮廓是颅面相似性评估中最重要的因素。这一结论与面部识别心理实验的结论以及我们的主观测试结果一致。这些结果可能为三维或二维面部相似性评估、分析和面部识别提供重要指导。
