基于曲率的鼻尖点定位在距离图像中处理三维人脸

D. Mukherjee, D. Bhattacharjee, M. Nasipuri
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引用次数: 1

摘要

不受约束地从任意对象获取数据导致面部扫描具有显著的姿势变化。3D人脸识别的挑战分为两个主要阶段,即预处理距离扫描以检测基准检测,同时识别/填充由于遮挡和异常噪声降低而缺失的部分,以及在后处理期间与存储模型进行实际匹配。本文提出了一种在预处理阶段利用HK曲率对三维人脸图像鼻尖基点进行定位的算法。在归一化步骤之后,对三维数据计算曲率。HK曲率分类对人脸进行潜在区域分割,并进一步进行形态学增强。分别对椭圆凸、椭圆凹、双曲凸、双曲凹增强曲率轮廓进行了加工。利用积分图像技术对曲率图像进行了从粗到细的尺度空间处理。使用启发式驱动的模板包规则来增强本地化。该方法在Gavabdb和FRAV3D人脸数据库上实现了高达90%的鼻尖定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Curvature based localization of nose tip point for processing 3D-face from range images
Unconstrained acquisition of data from arbitrary subjects results in facial scans with significant pose variations. The challenges in 3D face recognition are into two main stages, namely preprocessing range scans for detection of fiducial detection while identifying/filling missing parts due to occlusions along with outlier noise reduction and during post-processing where actual match is done with stored models. In this work, an algorithm using HK curvature for localization of nose tip fiducial point on 3D-face image is proposed at preprocessing stage. Curvature is evaluated on 3D data following the normalization step. HK curvature classification results potential region segmentation on face and operated further with morphological enhancements. Four types of curvatures- elliptical convex, elliptical concave, hyperbolic convex and hyperbolic concave enhanced curvature profiles are being processed separately. Coarse-to-fine scale space using integral images technique is applied on the curvature images. Localization is boosted using a heuristic driven bag of templates rule. The proposed technique achieved up to 90% accurate nose-tip localization on Gavabdb and FRAV3D face database.
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