Many Face Recognition techniques focus on 2D-2D comparison or 3D-3D comparison, however few techniques explore the idea of cross-dimensional comparison. This paper presents a novel face recognition approach that implements cross-dimensional comparison to solve the issue of pose invariance. Our approach implements a Gabor representation during comparison to allow for variations in texture, illumination, expression and pose. Kernel scaling is used to reduce comparison time during the branching search, which determines the facial pose of input images. The conducted experiments prove the viability of this approach, with our larger kernel experiments returning 91.6% - 100% accuracy on a database comprised of both local data, and data from the USF Human ID 3D database.
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机译：许多人脸识别技术专注于2D-2D比较或3D-3D比较，但是很少有技术探索跨维度比较的想法。本文提出了一种新颖的人脸识别方法，该方法实现了多维比较以解决姿势不变性问题。我们的方法在比较过程中实现了Gabor表示，以允许纹理，照明，表情和姿势的变化。内核缩放用于减少分支搜索期间的比较时间，该比较时间确定了输入图像的面部姿势。进行的实验证明了这种方法的可行性，我们的大型内核实验在包含本地数据和USF Human ID 3D数据库数据的数据库上返回了91.6％-100％的准确性。
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