Non-reference Image Quality Assessment for Fingervein Presentation Attack Detection

Bhogal, Amrit Pal Singh; Söllinger, Dominik; Trung, Pauline; Hämmerle-Uhl, Jutta; Uhl, Andreas

doi:10.1007/978-3-319-59126-1_16

Cited by 6 publications

(12 citation statements)

References 19 publications

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“…Finger vein images are classified based on Image Quality Assessment (IQA) without giving any clear indication about the actual IQA used and any experimental results. In [21], the authors successfully apply general-purpose non-reference image quality metrics to discriminate real finger vein images from fake ones. Subsequent work [242] additionally applies natural scene statistics and looks into the issue of cross-sensor and crosssubject finger vein presentation attack detection.…”

Section: Presentation Attack Detectionmentioning

confidence: 99%

“…The proposed system is said to operate in a walk-through style, while this is not entirely clear from the description. 21…”

Section: Hand-based Vascular Traitsmentioning

confidence: 99%

“…https://mobileidworld.com/vein-id-iphone-905154/ 21. http://social-innovation.hitachi/us/case_studies/advanced-finger-vein-authenticationtechnology-opens-doors-for-you/ 22.…”

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confidence: 99%

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State of the Art in Vascular Biometrics

Uhl

2019

Advances in Computer Vision and Pattern Recognition

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The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

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Section: Presentation Attack Detectionmentioning

confidence: 99%

“…The proposed system is said to operate in a walk-through style, while this is not entirely clear from the description. 21…”

Section: Hand-based Vascular Traitsmentioning

confidence: 99%

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State of the Art in Vascular Biometrics

Uhl

2019

Advances in Computer Vision and Pattern Recognition

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“…Two years later, the first competition on finger vein PAD was organised [75], where three different teams participated. Since then, different PAD approaches have been presented, based on either a video sequence and motion magnification [60], texture analysis [44,61,71], image quality metrics [7], or more recently, neural networks [52,59,63] and image decomposition [58].…”

Section: Introductionmentioning

confidence: 99%

Presentation Attack Detection for Finger Recognition

Kolberg

Gomez‐Barrero

Venkatesh

et al. 2019

Advances in Computer Vision and Pattern Recognition

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Whereas other biometric characteristics, such as the face, are readily available for an eventual attacker through social media or easy to capture with a conventional smartphone, vein patterns can only be acquired with dedicated sensors. This fact makes them relevant not only for recognition purposes but especially for Presentation Attack Detection (PAD), for instance, in combination with fingerprint recognition. In this chapter, we make use of this combination and present a finger vein-based PAD algorithm to detect presentation attacks targeting fingerprint recognition. The experiments are carried out on a newly collected database, comprising 32 species of Presentation Attack Instruments ranging from printed artefacts to more sophisticated fingerprint overlays. The results show that our method preserves a convenient usage while detecting around 90% of the attacks. However, thin and transparent fingerprint overlays remain very challenging.

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“…Moreover, Dodge and Karam [11] demonstrated that deep neural networks are susceptible to image quality distortions, particularly to blur and noise. Another examples of known CV algorithms that are affected by the quality of the input images include finger vein detection [12], biometric sensor spoofing [13], face recognition [14], video stream recognition systems [15], deep learning reconstruction of magnetic resonance imaging (MRI) [16], and multi-view activity recognition [17]. Object detection using YOLO [18] on the distorted (left) and pristine (right) images, taken from GoPro [19] dataset.…”

Section: Introductionmentioning

confidence: 99%

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

et al. 2018

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Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.The filters W i are learned via independent component analysis (ICA). The binarized features b i are aggregated following the same procedure described for generating the LBP labels. The descriptive features are obtained by computing the histogram of the aggregated data.Similarly to the LBP, which generates LBP channels, the BSIF generates coded images. These coded images are the set of labels generated after the binarized features are computed using Equation (17) and aggregated using Equation (1). The aggregation of BSIF results is based on a selected number of bits, instead of the number of neighbors of the labeled pixel. The labeling depends on the relationship between the patch size l and the number of binarized features b i . Figure 6 shows the BSIF coded images corresponding to the same reference using different BSIF parameters. As can be seen in this figure, the textured information depends on the patch size l and on the number of bits. The number of bits is less or equal l 2 − 1. This is the reason why the second column does not contain BSIF coded images for 9, 10, 11, or 12 bits. Figure 6 shows that the choice of the number of bits and patch sizes is important for texture analysis algorithms. Therefore, multiscale approaches that incorporate several combinations of these parameters are interesting [74][75][76][77].

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Non-reference Image Quality Assessment for Fingervein Presentation Attack Detection

Cited by 6 publications

References 19 publications

State of the Art in Vascular Biometrics

State of the Art in Vascular Biometrics

Presentation Attack Detection for Finger Recognition

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

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