A technique to match highly similar 3D objects with an application to biomedical security

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.