论文标题
使用3D面部建模技术和深度学习方法生成热图像数据样品
Generating Thermal Image Data Samples using 3D Facial Modelling Techniques and Deep Learning Methodologies
论文作者
论文摘要
生成合成数据的方法已变得越来越重要，以构建基于卷积神经网络（CNN）为广泛的计算机视觉应用所需的大型数据集（CNN）所需的深度数据集。在这项工作中，我们扩展了现有方法，以显示如何映射2D热面部数据以提供3D面部模型。在拟议的研究工作中，我们使用塔夫茨数据集通过使用单个额叶姿势来产生3D的面部姿势。该系统通过通过基于融合的图像预处理操作来完善现有的图像质量来起作用。精制的输出具有更好的对比度调整，噪声水平降低和黑暗区域的曝光度更高。与原始数据相比，它使人脸的面部标志性和温度模式更加明显和可见。不同的图像质量指标用于将图像的精制版本与原始图像进行比较。在拟议的研究的下一阶段中，使用卷积神经网络（CNN）来创建3D面部几何结构。然后将生成的输出进口在Blender软件中，以最终提取男性和女性的3D热面输出。在室内实验室环境中，使用原型热摄像机（在Heliaus Eu项目中开发的原型热摄像头（在Heliaus Project下）获得的热面数据也使用了相同的技术，然后将其用于生成合成3D面部数据，以及不同的Yaw Yaw Face角度以及最后的面部深度图。
Methods for generating synthetic data have become of increasing importance to build large datasets required for Convolution Neural Networks (CNN) based deep learning techniques for a wide range of computer vision applications. In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models. For the proposed research work we have used tufts datasets for generating 3D varying face poses by using a single frontal face pose. The system works by refining the existing image quality by performing fusion based image preprocessing operations. The refined outputs have better contrast adjustments, decreased noise level and higher exposedness of the dark regions. It makes the facial landmarks and temperature patterns on the human face more discernible and visible when compared to original raw data. Different image quality metrics are used to compare the refined version of images with original images. In the next phase of the proposed study, the refined version of images is used to create 3D facial geometry structures by using Convolution Neural Networks (CNN). The generated outputs are then imported in blender software to finally extract the 3D thermal facial outputs of both males and females. The same technique is also used on our thermal face data acquired using prototype thermal camera (developed under Heliaus EU project) in an indoor lab environment which is then used for generating synthetic 3D face data along with varying yaw face angles and lastly facial depth map is generated.
