Sequeira Ana F, Cardoso Jaime S
INESC TEC-INESC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, Porto 4200-465, Portugal.
Departamento de Engenharia Eletrotécnica e de Computadores, Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto 4200-465, Portugal.
Sensors (Basel). 2015 Jun 19;15(6):14615-38. doi: 10.3390/s150614615.
Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.
指纹活体检测方法的开发旨在克服指纹生物识别系统易受伪造攻击的弱点。传统方法对入侵者的行为相当乐观，假定其使用的是先前已知的材料。这种假设导致使用监督技术来评估方法的性能，使用真实样本和伪造样本训练预测模型，并分别评估每种类型的伪造样本。此外，样本表示中常常包含背景，这完全扭曲了决策过程。因此，我们建议应执行自动分割步骤，将指纹与背景分离，从而真正判定指纹的活体性，而非背景的特征。此外，我们认为无法完全对伪造样本进行建模，因为入侵者使用的材料事先未知。我们通过对真实样本的分布进行建模，并将与该模型极不相符的样本预测为伪造样本，来进行设计。我们的实验比较了监督方法与仅依赖真实样本的半监督方法的性能。所得结果与更标准的方法不同，这强化了我们的信念，即文献中的结果误导性地估计了生物识别系统的真正脆弱性。
