OCT Fingerprint Presentation Attack Detection Using Frequency Disentangling Features

In automated fingerprint recognition systems（AFRSs），the development of fingerprint anti-spoofing ability is very crucial. Traditional fingerprints are usually obtained by surface fingerprint imaging， and such texture features are easy to be stolen. Fake fingerprints made of low-cost materials，such as artificial fingerprints made of 2D printing，silicone and other materials can easily attack these AFRSs. Therefore，using these traditional fingerprints for recognition will be difficult to detect presentation attacks. Existing research generally focuses on fingerprint modes with anti-counterfeiting features， such as high-resolution fingerprints with sweat gland characteristics and fingerprints with finger vein characteristics to develop presentation attack detection algorithms. This paper proposes a novel Optical Coherence Technology（OCT）-based fingerprint Presentation Attack Detection（PAD）method from the frequency domain to further improve the capability of fingerprint attack detection. OCT fingerprint imaging is a three-dimensional imaging technique that can capture subsurface fingerprint information beneath the fingertip's epidermis. An OCT fingerprint data is presented in the form of multiple cross-sectional images（i. e. B-scan），which can reflect multiple layers of biometric structure. It is very different from the surface image of a fingerprint. However， the existing PAD methods based on OCT fingerprint are traditional manual feature extraction methods and time-domain learning-based methods. Handcrafted extraction of fixed features in OCT fingerprint images is easily affected by noise， and these methods are not robust enough. Learning-based methods can learn the distribution of genuine and fake fingerprints and obtain more robust information representation in PAD. However，the information distribution in the image is superimposed，which may be ignored in the time-domain methods. Different from previous approaches， we first design a Frequency Feature Disentangling（FFD）model using convolutional neural networks and residual structures to decompose OCT-based fingerprint B-scans into four different frequency subbands like Discrete Wavelet Transform （DWT）. Through such disentangling， information superimposed in the original image in the spatial domain（e. g.， discriminative PAD feature， invalid and redundant feature）can be separated respectively. We then let it learn different frequency codes to form their corresponding latent codes. Finally， the spoofness score which is used to distinguish PAs from bonafides is designed based on the latent codes. The experimental results on the commonly used OCT fingerprint dataset，evaluated on the dataset with 93 200 bonafide B-scans from 137 fingers and 48 400 B-scans from 121 PAs， show that our method can effectively preserve the most significant discriminative features and remove some useless interference information superimposed in the spatial domain by disentangling into the frequency domain for eliminating interference and effective PAD. In the performance comparison experiment with existing PAD methods， the proposed method achieves a minimum error（Err.）of 0. 67%，which reduces the minimum error by 3. 03% and improves the performance by 81. 89% compared with the existing time-domain based state-of-the-art（SOTA）method， and there is a difference of only 0. 4s in computing consumption. Additionally， we also compare the performance of the proposed method with the SOTA method in different attack materials. The proposed method achieves superior performance in both 2D and 3D attack materials， with a 3. 72% reduction in Err. compared to the SOTA method specifically for 2D attack materials.