Sex classification is a challenging open problem in computer vision. It is useful from statistics up to people recognition on surveillance video. So far, the best performance can be achieved by using 3D cameras, but this approach requires the use of some especial hardware. Other 2D approaches achieve good results on normal situations but fail when the person wears loose clothing and carries bags or the camera angle changes as they rely on calculating borders, silhouettes, or the energy of the person in the image. This work aims to provide a novel sex classification methodology based on the creation of a virtual skeleton for each individual from 2D images and video; then, the distances between some points of the skeleton are measured and work as input of a sex classifier. This improves the results since clothing, bags, and the camera angle affect little the skeletonization process.

中文翻译：

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通过2D骨骼化进行性别分类

性别分类是计算机视觉中一个具有挑战性的开放性问题。从统计数据到人们对监视视频的识别，它都是有用的。到目前为止，使用3D相机可以达到最佳性能，但是这种方法需要使用某些特殊的硬件。其他2D方法在正常情况下可以取得良好的效果，但是当人穿着宽松的衣服并背着包或相机角度发生变化（因为他们依赖于计算图像中的人的边界，轮廓或能量）时，这种方法会失败。这项工作旨在提供一种新颖的性别分类方法，该方法基于从2D图像和视频为每个人创建虚拟骨骼的基础上创建；然后，测量骨骼的某些点之间的距离，并将其用作性别分类器的输入。由于服装，箱包，