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首页> 外文会议>International visual informatics conference >Intrinsic Face Image Decomposition from RGB Images with Depth Cues
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Intrinsic Face Image Decomposition from RGB Images with Depth Cues

机译：具有深度提示的RGB图像的内在人脸图像分解

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摘要

As a pre-step of reconstructing face attributes technology, the quality of face intrinsic image decomposition result has a direct impact on the sub-operations of reconstructing face attributes detail. There are two challenging problems with the intrinsic face image decomposition methods which are the quality of face-base intrinsic image, and the details of the shading image. In this study a new image model for intrinsic face image decomposition from RGB images with depth cues is proposed to produce high quality results even with simple constraints. The proposed model consists of three main steps: face cropping operation, processing the RGB color normalization, and the super-pixel segmentation. The face image is first cropped to get face area, then a color normalization process for the cropped face image is used to normalize RGB pixels, and finally the super-pixel segmentation based on mean shift algorithm is applied which has a good performance on reduce artifact and shading image's detail retention. To evaluate the proposed model, both qualitative and quantitative assessments be used. The qualitative assessment is based on human subjective visual standards to compare the intrinsic images results, and the quantitative assessment is based on the data analyze of the image information entropy. Qualitative and quantitative results both demonstrate that the performance of the proposed model is better than other techniques in the field of intrinsic face image decomposition.
机译：人脸固有图像分解结果的质量作为重建人脸属性技术的第一步，直接影响人脸属性细节重建的子操作。固有面部图像分解方法存在两个具有挑战性的问题，即基于面部的固有图像的质量和阴影图像的细节。在这项研究中，提出了一种新的图像模型，该模型可以从具有深度提示的RGB图像中分解出内在的人脸图像，从而即使在简单的约束下也能产生高质量的结果。所提出的模型包括三个主要步骤：面部裁剪操作，处理RGB颜色归一化和超像素分割。首先对人脸图像进行裁切得到人脸区域，然后对裁切后的人脸图像进行色彩归一化处理，对RGB像素进行归一化处理，最后应用基于均值漂移算法的超像素分割，在减少伪影方面具有良好的表现。和阴影图像的细节保留。为了评估所提出的模型，可以使用定性和定量评估。定性评估基于人类主观视觉标准来比较内在图像结果，定量评估基于图像信息熵的数据分析。定性和定量结果均表明，所提出的模型在固有面部图像分解领域的性能优于其他技术。

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