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A technique to match highly similar 3D objects with an application to biomedical security
Multimedia Tools and Applications ( IF 3.6 ) Pub Date : 2021-01-19 , DOI: 10.1007/s11042-020-10161-8
Akhilesh Mohan Srivastava , Arushi Jain , Priyanka Rotte , Surya Prakash , Umarani Jayaraman

Biometric technologies such as the face, fingerprint, and iris recognition have important utility in biomedical and healthcare applications. The use of biometrics in these applications ensures that critical medical information and access to secure premises and medical instruments is given only to authorized persons. In the past, the 2D face has been reliably used as biometrics in biomedical and healthcare applications. Though it provides remarkable performance in normal scenarios, the performance deteriorates in the presence of poor illumination, pose variations, and occlusions. These challenges are overcome by 3D face biometrics, where 3D face data (which provides complete geometric information of the face) is used in place of 2D face images. In this work, we develop a generic technique for matching of highly similar 3D objects and demonstrate its use in 3D face biometrics. The proposed technique combines the object classification utility from PointNet with One-Shot Learning from Siamese Network that converts the multi-class classification problem to a binary classification problem. We also propose a novel data augmentation technique that uses sub-sampling from the existing 3D data to increase the size and variability of the data, which is otherwise limited. Experimental results show that the proposed technique is considerably fast and accurate in the matching of highly similar 3D objects such as 3D human faces. It is also found to be highly efficient in terms of time and space and hence can be employed in designing real-time security solutions for biomedical, healthcare, and several applications in other fields.



中文翻译:

一种将高度相似的3D对象与生物医学安全性应用相匹配的技术

诸如面部,指纹和虹膜识别等生物识别技术在生物医学和医疗保健应用中具有重要的用途。在这些应用程序中使用生物识别技术可确保仅授权人员才能获得关键医疗信息以及对安全场所和医疗器械的访问权限。过去,二维面部已被可靠地用作生物医学和医疗保健应用中的生物特征。尽管它在正常情况下提供了出色的性能,但在照明不佳,姿势变化和遮挡的情况下,性能会下降。通过3D人脸生物识别技术克服了这些挑战,其中使用3D人脸数据(提供人脸的完整几何信息)代替2D人脸图像。在这项工作中 我们开发了一种用于匹配高度相似的3D对象的通用技术,并展示了其在3D面部生物特征识别中的使用。所提出的技术将PointNet的对象分类实用程序与Siamese网络的一键式学习相结合,将多类分类问题转换为二进制分类问题。我们还提出了一种新颖的数据增强技术,该技术使用来自现有3D数据的子采样来增加数据的大小和可变性,否则将受到限制。实验结果表明,所提出的技术在匹配高度相似的3D对象(例如3D人脸)时非常快速且准确。还发现它在时间和空间方面非常高效,因此可用于设计生物医学,医疗保健,

更新日期:2021-01-19
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