Next Article in Journal
A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor
Previous Article in Journal
CuFusion: Accurate Real-Time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(10), 2261;

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
Author to whom correspondence should be addressed.
Received: 15 August 2017 / Revised: 27 September 2017 / Accepted: 27 September 2017 / Published: 1 October 2017
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [4391 KB, uploaded 9 October 2017]   |  


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. View Full-Text
Keywords: NIR camera-based finger-vein recognition; spoof detection; presentation attack detection; convolutional neural network; transfer learning NIR camera-based finger-vein recognition; spoof detection; presentation attack detection; convolutional neural network; transfer learning

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Nguyen, D.T.; Yoon, H.S.; Pham, T.D.; Park, K.R. Spoof Detection for Finger-Vein Recognition System Using NIR Camera. Sensors 2017, 17, 2261.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top