Gender Classification Based on Spatio-Frequency Feature Fusion of OCT Fingerprint Images in the IoT Environment

作者：Lingzhen Kong, Kangkang Liu, Xiyuan Hu, Ning Zhang, Lianyong Qi, Xiangrui Li, Xiaokang Zhou · 发表于：IEEE Internet of Things Journal · 年份：2024 · DOI：10.1109/jiot.2024.3381428 · 被引用次数：13 · 研究领域：Optical Coherence Tomography Applications、Biometric Identification and Security、Retinal Imaging and Analysis

In the rapidly evolving landscape of the Internet of Things (IoT), concerns about privacy and security have become significant as interconnected devices communicate and collaborate. Fingerprints, serving as unique biometric identifiers, play a crucial role in the authentication and identification processes within this interconnected and exchanged network. However, attention is often directed towards the disclosure of visible fingerprints, overlooking latent fingerprints. This is primarily due to the challenges involved in extracting latent fingerprints, especially those remaining on the adhesive side of tape. Traditional methods physically/chemically peel tape to extract these fingerprints, but cause irreversible damage to the tape, hindering accurate fingerprint extraction. In this context, our investigation reveals that Optical Coherence Tomography (OCT) technology allows for the extraction of high-quality OCT fingerprint images from the adhesive side of tape, yielding precise fingerprint recognition and gender classification results. Concretely, we build a novel type of robotic-arm spectral-domain OCT (SD-OCT), which is software-controlled for the movement of the sample arm, making sample scanning more flexible and efficient. Furthermore, we utilize a deep learning network to perform representation learning on OCT fingerprints for the purpose of gender classification. In the first branch, we input OCT fingerprints into an EfficientNet-B3 network to learn their spatial doma...