面部皮肤分析检测黑眼圈和痤疮

Kiran M K, Kusum Meda Ravi, U. V
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引用次数: 1

摘要

这项研究的目的是分析和检测最常见的面部皮肤状况,如寻常痤疮和黑眼圈。在皮肤分析算法中,使用现有的计算机视觉算法(如Otsu的阈值算法和深度学习(DL)技术)来检测面部痤疮和黑眼圈的出现。这些技术被进一步修改以适应准备好的数据集,并获得更大的评估指标价值。本文提出了两种检测黑眼圈的技术,即肤色像素值差异和阈值分割技术,并比较了它们的性能。在阈值技术中,获得的IoU为0.737,可以更好地显示受影响区域。此外,痤疮检测使用两个深度学习骨干,即Inception ResNet 50和MobileNet。两种方法的准确度均为99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facial Skin Analysis for Detection of Dark Circles and Acne
The aim of this research is to analyze and detect the most commonly found facial skin conditions like acne vulgaris and dark circles. In the skin analysis algorithm, the occurrences of facial acne and dark circles are detected by using existing Computer Vision algorithms such as Otsu’s thresholding algorithm and Deep Learning (DL) techniques. These techniques are further modified to suit the prepared dataset and achieve greater value of evaluation metrics. This article proposes two techniques for the detection of dark circles, which are the difference in Skin Tone Pixel Values and the Thresholding Technique and compare their performance. In the Thresholding Technique, the IoU obtained was 0.737, which provided better visualization of the affected region. Further, acne detection was carried out using two deep learning backbones viz, Inception ResNet 50 and MobileNet. The accuracy obtained for both the methods was 99%.
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