Human Recognition Using Face in Computed Tomography

TL;DR

This paper presents a novel method for patient identification using facial features extracted from 3D CT images, employing deep learning and transfer learning techniques to improve accuracy and robustness.

Contribution

It introduces an automatic pipeline for face-based biometric recognition in 3D CT images, addressing data sparsity and inter-class discrimination challenges.

Findings

Achieved 92.53% 1:56 identification accuracy

Achieved 96.12% 1:1 verification accuracy

Outperformed existing methods in recognition tasks

Abstract

With the mushrooming use of computed tomography (CT) images in clinical decision making, management of CT data becomes increasingly difficult. From the patient identification perspective, using the standard DICOM tag to track patient information is challenged by issues such as misspelling, lost file, site variation, etc. In this paper, we explore the feasibility of leveraging the faces in 3D CT images as biometric features. Specifically, we propose an automatic processing pipeline that first detects facial landmarks in 3D for ROI extraction and then generates aligned 2D depth images, which are used for automatic recognition. To boost the recognition performance, we employ transfer learning to reduce the data sparsity issue and to introduce a group sampling strategy to increase inter-class discrimination when training the recognition network. Our proposed method is capable of capturing underlying identity characteristics in medical images while reducing memory consumption. To test its effectiveness, we curate 600 3D CT images of 280 patients from multiple sources for performance evaluation. Experimental results demonstrate that our method achieves a 1:56 identification accuracy of 92.53% and a 1:1 verification accuracy of 96.12%, outperforming other competing approaches.