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首页> 外文期刊>Journal of visual communication & image representation >Task-based evaluation of skin detection for communication and perceptual interfaces
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Task-based evaluation of skin detection for communication and perceptual interfaces

机译：基于任务的皮肤检测，用于交流和感知界面的评估

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摘要

Skin detection is frequently used as the first step for the tasks of face and gesture recognition in perceptual interfaces for human-computer interaction and communication. Thus, it is important for the researchers using skin detection to choose the optimal method for their specific task. In this paper, we propose a novel method of measuring the performance of skin detection for a task. We have created an evaluation framework for the task of hand detection and executed this assessment using a large dataset containing 17 million pixels from 225 images taken under various conditions. The parameter set of the skin detection has been trained extensively. Five colorspace transformations with and without the illuminance component coupled with two color modeling approaches have been evaluated. The results indicate that the best performance is achieved by transforming to SCT colorspace, using the illuminance component, and modeling the distribution with the histogram approach. Some conclusions such as the SCT colorspace being one of the best colorspaces are consistent with our previous work, while findings such as the YUV colorspace performing well in this work when it was one of the worst in our previous work are different. This indicates that the performance measured at the pixel-level might not be the ultimate indicator for the performance at the task-level of hand detection. We believe that the users of skin detection will find our task-based results to be more relevant than the traditional pixel-level results. However, we acknowledge that an evaluation is limited by its specific dataset and evaluation protocols.
机译：皮肤检测通常被用作人机交互和通信的感知界面中的面部和手势识别任务的第一步。因此，对于使用皮肤检测的研究人员来说，选择适合其特定任务的最佳方法非常重要。在本文中，我们提出了一种新的方法来测量任务的皮肤检测性能。我们创建了一个用于手部检测任务的评估框架，并使用包含来自在不同条件下拍摄的225张图像中的1,700万像素的大型数据集执行了此评估。皮肤检测的参数集已得到广泛培训。已经评估了具有和不具有照度分量的五种颜色空间转换以及两种颜色建模方法。结果表明，通过使用照度分量转换为SCT色彩空间并使用直方图方法对分布进行建模，可以实现最佳性能。一些结论，例如SCT色彩空间是最好的色彩空间之一，与我们以前的工作是一致的，而诸如YUV色彩空间的发现在我们的先前工作中是最差的颜色之一时在这项工作中表现不错。这表明在像素级测量的性能可能不是手部检测任务级性能的最终指标。我们相信，皮肤检测的用户会发现我们基于任务的结果比传统的像素级结果更相关。但是，我们承认评估受到其特定数据集和评估协议的限制。

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