论文标题
可以仅使用2D身体关键点估计来学习人性吗？
Can Human Sex Be Learned Using Only 2D Body Keypoint Estimations?
论文作者
论文摘要
在本文中，我们分析了男性和女性性别识别问题，并仅使用2D关键来提出全自动分类系统。关键点代表人类关节。关键点集由15个接头组成，使用openpose 2D键盘检测器获得关键点估计。我们学习了一个深度学习模型，可以使用按键作为输入和二进制标签作为输出来区分男性和女性。我们在实验部分中使用两个公共数据集-3DPEOPLE和PETA。在PETA数据集上，我们报告了77％的准确性。我们提供有关PETA和3DPEOPLE的模型性能细节。为了衡量嘈杂的2D关键点检测对性能的影响，我们在3DPEOPLE地面真相和嘈杂的关键点数据上进行了单独的实验。最后，我们提取一组影响分类准确性并提出未来工作的因素。该方法的优点是输入很小，架构很简单，这使我们能够运行许多实验并将实时性能保持推理。带有实验和数据准备脚本的源代码可在GitHub（https://github.com/kristijanbartol/human-sex-classifier）上获得。
In this paper, we analyze human male and female sex recognition problem and present a fully automated classification system using only 2D keypoints. The keypoints represent human joints. A keypoint set consists of 15 joints and the keypoint estimations are obtained using an OpenPose 2D keypoint detector. We learn a deep learning model to distinguish males and females using the keypoints as input and binary labels as output. We use two public datasets in the experimental section - 3DPeople and PETA. On PETA dataset, we report a 77% accuracy. We provide model performance details on both PETA and 3DPeople. To measure the effect of noisy 2D keypoint detections on the performance, we run separate experiments on 3DPeople ground truth and noisy keypoint data. Finally, we extract a set of factors that affect the classification accuracy and propose future work. The advantage of the approach is that the input is small and the architecture is simple, which enables us to run many experiments and keep the real-time performance in inference. The source code, with the experiments and data preparation scripts, are available on GitHub (https://github.com/kristijanbartol/human-sex-classifier).
