Astawa I Nyoman Gede Arya, Putra I Ketut Gede Darma, Sudarma Made, Hartati Rukmi Sari
Department of Electrical Engineering, Politeknik Negeri Bali, Bali, Indonesia.
Information of Technology, Faculty of Engineering, Udayana University, Bali, Indonesia.
Data Brief. 2020 May 13;31:105677. doi: 10.1016/j.dib.2020.105677. eCollection 2020 Aug.
KomNet is a face image dataset originated from three media sources which can be used to recognize faces. KomNET contains face images which were collected from three different media sources, i.e. mobile phone camera, digital camera, and media social. The collected face dataset was frontal face image or facing the camera. The face dataset originated from the three media were collected without certain conditions such as lighting, background, haircut, mustache and beard, head cover, glasses, and differences of expression. KomNet dataset were collected from 50 clusters in which each of them consisted of 24 face images. To increase the number of training data, the face images were propagated with augmentation image technique, in which ten augmentations were used such as Rotate, Flip, Gaussian Blur, Gamma Contrast, Sigmoid Contrast, Sharpen, Emboss, Histogram Equalization, Hue and Saturation, Average Blur so the face images became 240 face images per cluster. The author trained the dataset by using CNN-based transfer learning VGGface. KomNET dataset are freely available on https://data.mendeley.com/datasets/hsv83m5zbb/2.
KomNet是一个源自三个媒体来源的面部图像数据集，可用于人脸识别。KomNET包含从三个不同媒体来源收集的面部图像，即手机摄像头、数码相机和社交媒体。收集到的面部数据集为正面面部图像或面向摄像头的图像。源自这三种媒体的面部数据集是在没有特定条件的情况下收集的，如光照、背景、发型、胡须、头巾、眼镜和表情差异等。KomNet数据集是从50个聚类中收集的，每个聚类由24张面部图像组成。为了增加训练数据的数量，使用增强图像技术对面部图像进行扩充，其中使用了十种增强方法，如旋转、翻转、高斯模糊、伽马对比度、西格蒙德对比度、锐化、浮雕、直方图均衡化、色调和饱和度、均值模糊，这样每个聚类的面部图像就变成了240张。作者使用基于卷积神经网络（CNN）的迁移学习VGGface对该数据集进行了训练。KomNET数据集可在https://data.mendeley.com/datasets/hsv83m5zbb/2上免费获取。
