The greater sciatic notch (GSN) is one of the most important and frequently used characteristics for determining the sex of skeletons, but objective assessment of this characteristic is not without its difficulties. We tested the robustness of GSN sex classification on the basis of geometric morphometrics (GM) and support vector machines (SVM), using two different population samples. Using photographs, the shape of the GSN in 229 samples from two assemblages (documented collections of a Euroamerican population from the Maxwell Museum, University of New Mexico, and a Hispanic population from Universidad Nacional Autónoma de México, Mexico City) was segmented automatically and evaluated using six curve representations. The optimal dimensionality for each representation was determined by finding the best sex classification. The classification accuracy of the six curve representations in our study was similar but the highest and concurrently homologous cross-validated accuracy of 92% was achieved for a pooled sample using Fourier coefficient and Legendre polynomial methods. The success rate of our classification was influenced by the number of semilandmarks or coefficients and was only slightly affected by GSN marginal point positions. The intrapopulation variability of the female GSN shape was significantly lower compared with the male variability, possibly as a consequence of the intense selection pressure associated with reproduction. Males were misclassified more often than females. Our results show that by using a suitable GSN curve representation, a GM approach, and SVM analysis, it is possible to obtain a robust separation between the sexes that is stable for a multipopulation sample.
Keywords: SVM learning model; curve segmentation; hip bone; sex assessment; shape analysis.
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