Rendering or normalization? An analysis of the 3D-aided pose-invariant face recognition

Yuhang Wu, S. Shah, I. Kakadiaris
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引用次数: 23

Abstract

In spite of recent progress achieved in near-frontal face recognition, the problem of pose variations prevalent in 2D facial images captured in the wild still remains a challenging and unsolved issue. Among existing approaches of pose-invariant face recognition, 3D-aided methods have been demonstrated effective and promising. In this paper, we present an extensive evaluation of two widely adopted frameworks of 3D-aided face recognition in order to compare the state-of-the-art, identify remaining issues, and offer suggestions for future research. Specifically, we compare the pose normalization and the pose synthesis (rendering) based methods in an empirical manner. The database (UHDB31) that we use covers 21 well-controlled pose variations, half of which show a combination of yaw and pitch. Through the experiments, we present the advantages and disadvantages of these two methods to provide solid data for future research in 3D-aided pose-invariant face recognition.
呈现还是规范化?三维辅助姿态不变人脸识别分析
尽管最近在近正面人脸识别方面取得了进展,但在野外捕获的2D人脸图像中普遍存在的姿势变化问题仍然是一个具有挑战性和未解决的问题。在现有的姿态不变人脸识别方法中,三维辅助方法已被证明是有效的和有前途的。在本文中,我们对两种被广泛采用的3d辅助人脸识别框架进行了广泛的评估,以比较最新的技术,确定剩余的问题,并为未来的研究提供建议。具体来说,我们以经验的方式比较了姿态归一化和基于姿态合成(渲染)的方法。我们使用的数据库(UHDB31)涵盖了21种控制良好的姿势变化,其中一半显示偏航和俯仰的组合。通过实验,我们给出了这两种方法的优缺点,为未来3d辅助姿态不变人脸识别的研究提供了坚实的数据。
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