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With the increasing integration of smartphones into our daily lives, fingerphotos are becoming a potential contactless authentication method. While it offers convenience, it is also more vulnerable to spoofing using various presentation attack instruments (PAI). The contactless fingerprint is an emerging biometric authentication but has not yet been heavily investigated for anti-spoofing. While existing anti-spoofing approaches demonstrated fair results, they have encountered challenges in terms of universality and scalability to detect any unseen/unknown spoofed samples. To address this issue, we propose a universal presentation attack detection method for contactless fingerprints, despite having limited knowledge of presentation attack samples. We generated synthetic contactless fingerprints using StyleGAN from live finger photos and integrating them to train a semi-supervised ResNet-18 model. A novel joint loss function, combining the Arcface and Center loss, is introduced with a regularization to balance between the two loss functions and minimize the variations within the live samples while enhancing the inter-class variations between the deepfake and live samples. We also conducted a comprehensive comparison of different regularizations’ impact on the joint loss function for presentation attack detection (PAD) and explored the performance of a modified ResNet-18 architecture with different activation functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center loss. Finally, we evaluate the performance of the model using unseen types of spoof attacks and live data. Our proposed method achieves a Bona Fide Classification Error Rate (BPCER) of 0.12%, an Attack Presentation Classification Error Rate (APCER) of 0.63%, and an Average Classification Error Rate (ACER) of 0.37%.
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Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures
Fingerprint capture systems can be fooled by widely accessible methods to spoof the system using fake fingers, known as presentation attacks. As biometric recognition systems become more extensively relied upon at international borders and in consumer electronics, presentation attacks are becoming an increasingly serious issue. A robust solution is needed that can handle the increased variability and complexity of spoofing techniques. This paper demonstrates the viability of utilizing a sensor with time-series and color-sensing capabilities to improve the robust-ness of a traditional fingerprint sensor and introduces a comprehensive fingerprint dataset with over 36,000 image sequences and a state-of-the-art set of spoofing techniques. The specific sensor used in this research captures a traditional gray-scale static capture and a time-series color capture simultaneously. Two different methods for Presentation Attack Detection (PAD) are used to assess the benefit of a color dynamic capture. The first algorithm utilizes Static-Temporal Feature Engineering on the fingerprint capture to generate a classification decision. The second generates its classification decision using features extracted by way of the Inception V3 CNN trained on ImageNet. Classification performance is evaluated using features extracted exclusively from the static capture, exclusively from the dynamic capture, and on a fusion of the two feature sets. With both PAD approaches we find that the fusion of the dynamic and static feature-set is shown to improve performance to a level not individually achievable.
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- Award ID(s):
- 1650503
- PAR ID:
- 10136374
- Date Published:
- Journal Name:
- 12th IAPR International Conference On Biometrics
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICB45273.2019.8987297
