Fingerprint authentication is widely used thanks to its simple process and low cost, but it is vulnerable to fake fingerprints. Many researchers have been working on presentation attack detection to ensure the security of fingerprint systems. However, the existing studies only focus on improving the detection accuracy, let processing time and memory requirement be out of focus. Hence, it is difficult to integrate the existing algorithms to embedded and mobile systems. This study proposes a method to detect presentation attacks using a small fully convolutional network. The proposed network is designed using the structure of Fire module of SqueezNet. The use of Fire module results in a network which has around 0.5 million parameters. The proposed network is trained using images of 𝟑𝟐×𝟑𝟐,𝟒𝟖×𝟒𝟖, or 𝟔𝟒×𝟔𝟒 pixels. Since the network has no fully connected layer, it can do inference with images of any size. This advantage helps to improve the detection rate and allows the proposed algorithm to be integrated into fingerprint systems easily. The experiments show an average detection error of 1.43%, which is comparable with the state-of-the-art accuracy, while the processing time and memory requirement is much reduced.
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