The objective of this study is to demonstrate through empirical evaluation the potential of a number of computer vision (CV) methods for sex determination from human skull. To achieve this, six local feature representations, two feature learnings, and three classification algorithms are rigorously combined and evaluated on skull regions derived from skull partitions. Furthermore, we introduce for the first time the application of multi-kernel learning (MKL) on multiple features for sex prediction from human skull. In comparison to the classical forensic methods, the results in this study are competitive, attesting to the suitability of CV methods for sex estimation. The proposed approach is fully automatic.

中文翻译：

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用于颅骨性别估计的计算机视觉方法

这项研究的目的是通过经验评估来证明许多计算机视觉（CV）方法在确定人类头骨中的性别的潜力。为了实现这一目标，将六个局部特征表示，两个特征学习和三个分类算法严格组合在一起，并对源自头骨分区的头骨区域进行评估。此外，我们首次介绍了多核学习（MKL）在人类头骨性别预测的多种功能上的应用。与经典的法医方法相比，本研究的结果具有竞争力，证明了CV方法在性别估计中的适用性。所提出的方法是全自动的。