Recently, optical coherence tomography (OCT) has been used to noninvasively image the 3D structure of fingertip skin at high resolution. Unlike traditional 2D sensors (e.g., infrared light or capacitive technologies), the friction ridge information in 3D OCT fingerprint measurements requires reconstruction through layer segmentation. Accurate layer segmentation is helpful for fingerprint recognition and antispoofing applications. OCT volumes contain information corresponding to different directions that naturally provide complementary views. Inspired by this fact, we propose a novel orthogonal view-based attention network called OVA-Net, which exploits orthogonal views to learn the complementary information implied in the 3D fingerprint structure. Specifically, 3D convolutions and an A-line-based attention module are proposed in the B-scan view to model the long short-term intraslice correlations, whereas their counterparts in the C-scan view aim to model interslice correlations. An optical flow-based attention module is also proposed in the B-scan view to extract correlations between B-scans, which complements the interslice correlation learned in the C-scan view. Features from orthogonal views are progressively incorporated into a fusion pipeline for 3D layer segmentation. The effectiveness of OVA-Net is comprehensively evaluated in terms of layer segmentation accuracy, fingerprint reconstruction quality, and recognition performance.

Fingerprint presentation attack detection (FPAD) is essential in fingerprint identification systems. Noncontact methods such as fingerprint biometrics are becoming popular because they are not affected by skin conditions and there are no hygiene issues. However, most of the existing noncontact FPAD methods are supervised methods with poor generalizability and poor performance during events such as unseen presentation attacks (PAs). Moreover, easily overlooked frequency domain information contributes to the fingerprint antispoofing task. Therefore, we propose a wavelet-based memory-augmented autoencoder that fully utilizes the frequency domain information. Specifically, the model first decomposes the input image into high- and low-frequency information and extracts features separately. Subsequently, we propose a frequency complementary connection (FCC) module to realize the fusion and complementation of frequency domain information at the feature level. Moreover, a memory distance expansion loss is proposed to keep the memory module diverse. Experiments are conducted to verify the effectiveness of the method. The code of our model is available onhttps://github.com/SuperIOyht/WaveMemAE.