Fingerprint Presentation Attack Detection by Channel-Wise Feature Denoising

Liu, Feng; Kong, Zhe; Liu, Haozhe; Zhang, Wentian; Shen, Linlin

doi:10.1109/tifs.2022.3197058

Cited by 17 publications

(2 citation statements)

References 54 publications

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“…Its success has led to the development of Grad-CAM++ [ 26 ], which further enhances the explanation capabilities of Grad-CAM and has been used for object detection and localization [ 27 , 28 , 29 ]. The authors in [ 30 ] employed Grad-CAM as a visualization tool to identify and highlight noise across various channels of a network when processing a fingerprint image. The use of CAM is also presented in the study by [ 31 ], where it was used for patch extraction during the inference stage.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Ensemble Learning Using Explainable CNN for Spoof Fingerprints

Reza,

Jung

2023

Sensors

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Convolutional Neural Networks (CNNs) have demonstrated remarkable success with great accuracy in classification problems. However, the lack of interpretability of the predictions made by neural networks has raised concerns about the reliability and robustness of CNN-based systems that use a limited amount of training data. In such cases, the utilization of ensemble learning using multiple CNNs has demonstrated the capability to improve the robustness of a network, but the robustness can often have a trade-off with accuracy. In this paper, we propose a novel training method that utilizes a Class Activation Map (CAM) to identify the fingerprint regions that influenced previously trained networks to attain their predictions. The identified regions are concealed during the training of networks with the same architectures, thus enabling the new networks to achieve the same objective from different regions. The resultant networks are then ensembled to ensure that the majority of the fingerprint features are taken into account during classification, resulting in significant enhancement of classification accuracy and robustness across multiple sensors in a consistent and reliable manner. The proposed method is evaluated on LivDet datasets and is able to achieve state-of-the-art accuracy.

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Section: Related Workmentioning

confidence: 99%

Enhancing Ensemble Learning Using Explainable CNN for Spoof Fingerprints

Reza,

Jung

2023

Sensors

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“…Maheswari et al [ 18 ] proposed convolution neural network and dynamic differential annealing (CNN-DDA)-based spoofed fingerprint detection to analyze and evaluate fingerprint spoofing and forgery authentication systems. Kong et al [ 19 ] proposed a novel method for handling noisy information: channel-wise feature denoising for fingerprint presentation attack detection (CFD-PAD). The aforementioned methods are all based on surface fingerprints for anti-spoofing.…”

Section: Introductionmentioning

confidence: 99%