Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea.
Sensors (Basel). 2017 Oct 1;17(10):2261. doi: 10.3390/s17102261.
Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.
指纹静脉识别是一种新的先进的生物识别方法，由于其识别性能高、不易被盗以及由于皮肤状况缺陷而导致不准确的可能性较小等优点，引起了研究人员的关注。然而，正如之前的研究人员所报告的，有可能通过使用呈现攻击（伪造）指纹静脉图像来攻击指纹静脉识别系统。因此，在这种识别系统中需要进行欺骗检测，称为呈现攻击检测（PAD）。以前建立 PAD 方法的尝试主要集中在基于研究人员对真实（活体）和呈现攻击指纹静脉图像之间差异的观察，通过手工设计特征提取器（手工制作的特征提取器）。因此，检测性能受到限制。最近，深度学习框架已成功应用于计算机视觉领域，并在各种计算机视觉应用（例如基于图像的人脸识别、性别识别和图像分类）中，与传统的手工制作方法相比，提供了更优异的结果。在本文中，我们提出了一种使用卷积神经网络（CNN）的近红外（NIR）相机基于指纹静脉识别系统的 PAD 方法，以增强以前手工制作方法的检测能力。使用 CNN 方法，我们可以通过训练过程为 PAD 得出比其他手工制作方法更合适的特征提取器。我们进一步处理提取的图像特征，以增强 CNN 方法的呈现攻击指纹静脉图像检测能力，使用主成分分析方法（PCA）进行特征空间降维和支持向量机（SVM）进行分类。通过广泛的实验结果，我们确认我们提出的方法适用于呈现攻击指纹静脉图像检测，并且与基于 CNN 的方法和其他以前的手工制作方法相比，它可以提供更优异的检测结果。
