# Spoofed Fingerprint Image Detection Using Local Phase Patch Segment Extraction and a Lightweight Network

Sani Mohammed Abdullahi, Shuifa Sun, Asad Malik, Otabek Khudeyberdiev, Riskhan Basheer

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Sani Mohammed Abdullahi, Shuifa Sun, Asad Malik, Otabek Khudeyberdiev, Riskhan Basheer. Spoofed Fingerprint Image Detection Using Local Phase Patch Segment Extraction and a Lightweight Network. 18th IFIP International Conference on Digital Forensics (DigitalForensics), Jan 2022, Virtual, United States. pp.85-105, 10.1007/978-3-031-10078-9_5. hal-05315730

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Chapter 5

SPOOFED FINGERPRINT IMAGE
DETECTION USING LOCAL PHASE
PATCH SEGMENT EXTRACTION AND
A LIGHTWEIGHT NETWORK

Sani Mohammed Abdullahi, Shuifa Sun, Asad Malik, Otabek Khudey-
berdiev and Riskhan Basheer

**Abstract** Fingerprint spoofing is one of the most successful attacks on fingerprint biometric systems. It involves the presentation of a fake fingerprint to a biometric sensor, which recognizes it as the original template and consistently uses it to authenticate an impostor as the genuine owner of the template.

This chapter presents a methodology for combating fingerprint spoof-
ing that employs local phase patch segment extraction and a lightweight
triple-dense network. The methodology segments an input fingerprint
image using local phase patch segment extraction, which also assists
in extracting texture information so that each segment contains a con-
sistent number of patches and each patch contains adequate minutiae
information. The segmented image is fed to the lightweight triple-dense
network, which is designed to generate discriminative information that
helps distinguish between live and spoofed fingerprint images. This en-
sures optimum recognition accuracy and fast processing time while elim-
inating overfitting. Experimental evaluations using the LivDet 2013 and
LivDet 2015 fingerprint datasets reveal that the methodology accurately
classifies live and spoofed fingerprint images with an overall accuracy of
95.5%. Intra-class variation and inter-class similarity are eliminated by
generalization without any accuracy degradation.

**Keywords:** Fingerprint spoofing detection, neural network, local phase quantization

1. Introduction

As biometric recognition systems are increasingly used for authentica-
tion, passcodes and cards are rapidly becoming obsolete. However, the
vulnerability of biometric authentication systems to fingerprint spoofing attacks has raised concerns due to their deployment in systems for secure mobile transactions, border security checks, smartphone locks and myriad user identity and authentication applications [20].

Fingerprints have been spoofed using replicas of original fingerprints made from inexpensive materials such as Ecoflex, silicone, latex, gelatin, Play-Doh and wood glue. Meanwhile, advanced 3D printing techniques are increasingly used to produce high-quality spoofed fingerprints.

Fingerprint liveness detection, which analyzes live and spoofed fingerprints from diverse sources, is a recognized countermeasure against fingerprint spoofing. The two main classes of countermeasures involve hardware and software solutions [5, 18, 20]. A hardware-based fingerprint authentication system typically incorporates sensors (usually integrated in a fingerprint reader) that detect the liveness condition or vitality characterization of the skin by considering features such as blood flow, pulse, temperature and distortion. Fingerprint authentication systems that rely on special vitality characterization sensors are able to circumvent spoofing attacks. However, they are complex and upgrades can be expensive, leading to degradation in accuracy.

A software-based fingerprint authentication system uses features extracted from captured fingerprint images to differentiate between live and spoofed fingerprints. These solutions do not require the integration of special vitality characterization sensors. As a result, they are easier to update and more cost-effective while ensuring accuracy.

This chapter presents a software-based methodology for combating fingerprint spoofing that employs local phase patch segment extraction and a lightweight triple-dense network. The methodology segments an input fingerprint image using local phase patch segment extraction, which also assists in extracting texture information so that each segment contains a consistent number of patches and each patch contains adequate minutiae information. The segmented image is fed to the lightweight triple-dense network, which is designed to generate more discriminative information for distinguishing between live and spoofed fingerprint images. This ensures optimum recognition accuracy and fast processing time while avoiding overfitting.

## 2. Literature Review

Traditional software solutions employ techniques that rely on anatomical features of fingerprints (e.g., pores), physiological features (e.g., perspiration) and/or texture features. Marasco and Sansone [19] exploit robust morphological features and perspiration of presented fingerprints
to measure their vitality. Ghiani et al. [6] leverage the insensitivity of
local phase quantization to blurring effects to distinguish between live
and spoofed fingerprints; their approach yields promising results, but it
lacks a generalization capability. Nikam and Agarwal [24] have proposed
a technique that uses local binary pattern histograms to capture texture
details in fingerprint images such as orientation, smoothness, roughness
and regularity; although the technique provides good classification re-
sults, it cannot identify spoofed fingerprints. Jiang and Liu [16] improved
the technique of Nikam and Agarwal [24] using a uniform local binary
pattern in a spatial pyramid; the method achieves good invariance and
better accuracy.

Gragnaniello et al. [12] employ a local contrast phase descriptor gener-
ated from local amplitude contrast and behavior information to enhance
feature selection; a support vector machine based linear kernel classifier
is employed to differentiate between live and spoofed fingerprint images.
Xia et al. [29] employ a Weber local binary descriptor that utilizes dif-
ferential excitation and gradient orientation components from intensity
variance and orientation features, respectively; experiments performed
on two fingerprint liveness detection datasets yielded good accuracy.
Gonzalez-Soler et al. [10] combine three image representation approaches
by integrating local and global fingerprint information in the same fea-
ture space. The technique yields much better results compared with
other texture-based approaches, including the ability to detect unknown
spoofed fingerprints.

A limitation of texture-feature-based extraction techniques is their inability to generalize between live and spoofed fingerprints when unknown samples from different sensors are introduced. Deep learning approaches have been applied to address this limitation; they provide robust generalization capabilities by learning from high-level semantic features. Nogueira et al. [25], Pala and Bhanu [26] and Jang et al. [14] have employed convolutional neural networks for liveness detection. Their approaches yield reasonable detection accuracy, but they fall short of achieving a full generalization capability [4]. Chugh et al. [3] attempted to address the limitation using a convolutional neural network that exploits centered and aligned local patches through the use of fingerprint minutiae; the technique yields much better detection accuracy than its predecessor approaches.

Jung et al. [17] developed a novel approach for matching fingerprint
templates and their corresponding probe samples using a convolutional
neural network instead of merely classifying live and spoofed fingerprints.
Their approach, which uses two sequential networks, yields good detec-
tion accuracy without much increase in computational time. Yuan et
al. [30] proposed an improved convolutional neural network with image scale quantization to learn high-level semantic features through supervised learning. Their approach can handle scaling problems by transforming scaled images to a fixed scale, preserving the texture and quality information of features. However, the preservation of high-level information drastically reduces the generalization capability.

A major limitation of deep learning techniques is their high computational complexity and inability to efficiently work with fine-tuned network models because they are designed for digital images instead of fingerprints. To overcome these challenges, Zhang et al. [33] proposed a light-dense network that leverages the attention-pooling layer to overcome the shortcomings of global average-pooling. The technique, which integrates a residual path in dense blocks, achieves good generalization capability with low complexity. In other work, Zhang et al. [34] proposed the novel Slim-ResCNN architecture that incorporates a stacked series of improved residual blocks designed for fingerprint liveness detection. Finally, Park et al. [27] have proposed a tiny fully-convolutional network based on the SqueezeNet fire module structure with few parameters. Their approach achieves good detection accuracy, but it is not well-tuned to fingerprint images.

This research attempts to address the limitations in previous work by leveraging the advantages of texture-based solutions from handcrafted software techniques to fine-tune networks designed for digital images. Local phase patch segment extraction is applied to segment fingerprint images and extract level 1 texture features such as ridge valley patterns and orientation information. Next, the segmented region, identified as the region of interest (RoI) with rich information including minutiae, is input to a lightweight convolutional neural network architecture that leverages pooling and dense layers to ensure that the segmented minutiae regions assist in eliminating overfitting. This improves detection accuracy while providing a generalization capability and reduced computational complexity.

### 3. Proposed Methodology

The proposed methodology employs local phase patch segment extraction based on features generated from minutiae locations and orientations. High-level global semantic features are extracted using a lightweight custom network named FinSpoofNet. Since most convolutional neural network models [14, 17, 25] are built to accept input images that require fixed-length inputs of larger sizes, they may not be suitable for biometric images such as fingerprints. Therefore, re-
<figure><img src="image_3.png" /><figcaption>Figure 1. Fingerprint liveness detection methodology.</figcaption></figure>

searchers attempt to resize input samples via pre-processing operations such as cropping and scaling. However, these operations come with shortcomings. Cropping results in the loss of some texture information, which renders the original images incomplete. Scaling compresses images, leading to geometric distortions and reduction in image resolution. To address these shortcomings, after extracting the vital minutiae information, the methodology segments images to 112 × 112 pixels using local phase extraction so that the input images fit into the network structure. The idea of local phase patch segment extraction comes from local phase quantization [7], which extracts rich texture information and eliminates geometric distortions using the short-term Fourier transform. This concept inspired the use of phase segmentation, which ensures that the input images fit well into the architecture without losing vital information.

Figure 1 summarizes the fingerprint liveness detection methodology. Of all the anti-spoofing techniques based on convolutional neural networks, techniques that extract local patches around minutiae have proved to be efficient and robust against fabricated fingerprints. Therefore, the methodology is designed such that each segment contains a sufficient number of patches and each patch contains minutia information regardless of the minutia position in the patch.

## 3.1 Local Phase Feature Extraction

The term local phase patch segment extraction is drawn from local phase quantization, which is mainly applied in software-based liveness
detection techniques. Since generalized level feature extraction [2] is sought, local phase patch segment extraction begins by extracting the locations and orientations of minutiae features before segmentation.

**Minutiae Extraction.** The minutiae extraction algorithm specified in [1] is employed to detect minutiae information. Specifically, invariance operations are applied based on the reference minutiae $m_r = \{p_r, q_r, \theta_r\}$ where the set of minutiae locations from the given reference minutiae is $[p_i, q_i, \theta_i | i = 1, 2, \dots, N]$ in which $p_i, q_i$ and $\theta_i$ denote a minutia location and orientation, respectively.

**Local Phase Patch Extraction.** Local phase patch extraction employs a short-term Fourier transform based on local phase quantization, which is computed over each minutia patch of the local neighborhood $M_{P_x}$ at each minutia location of the image $I_x$. The short-term Fourier transform is given by:

$$ST_F(f, x) = \sum_{y \in M_{P_x}} V_w \cdot I(x - y) \partial^{-2\Pi k f^N y}$$

where $x_i = p_i, q_i, \theta_i$. Note that $x_i$ denotes the spatial coordinates $(p_i, q_i)$ and its orientation $(\theta_i)$ and $f$ is the frequency for defining the window vector $V_w$ that is used to determine the patch phase neighborhood $M_{P_x}$. The patch neighborhood also contains all the $x_i$ samples for each image $I_x = x^2$, which is given by:

$$M_{P_{x2}} = [M_{P_{x1}}, M_{P_{x2}}, \dots, M_{P_{x_n}}].$$

For effective segmentation of the phase patch extraction and to achieve the aforementioned invariance, the minutiae patch segments are set at $\partial^{-2\Pi k f^N x}$ for all the frequencies in $f = f_1, f_2, \dots, f_n$. Subsequently, eight local coefficients are considered to repeatedly compute the eight corresponding Fourier phase-frequency points on all the segments. Thus, the phases are given by:

$$f_1 = [\varphi_{(c_1)}, 0]^T, f_2 = [0, \varphi_{(c_2)}]^T, f_3 = [\varphi_{(c_1)}, \varphi_{(c_2)}]^T, f_4 = [\varphi_{(c_2)}, \varphi_{(c_1)}]^T, \\ f_5 = [\varphi_{(c_3)}, 0]^T, f_6 = [0, \varphi_{(c_4)}]^T, f_7 = [\varphi_{(c_3)}, \varphi_{(c_4)}]^T, f_8 = [\varphi_{(c_4)}, \varphi_{(c_3)}]^T$$

where $\varphi_{(c_n)}$ is a scalar phase in the patch corresponding to the transforms given in all the frequencies of a particular segment. Also, for each minutia orientation, the following vector representation of all the patch invariance is extracted:
<figure><img src="image_4.png" /><figcaption>Figure 2. Local phase patch segment extraction process.</figcaption></figure>

$$V_{\eta} = [ST_{F}(f_{1}, x), ST_{F}(f_{2}, x), ST_{F}(f_{3}, x), \dots, ST_{F}(f_{8}, x)].$$

Consequently, each phase patch $\varphi_{(c_i)}$ is set at a corresponding minutia location from all the patch neighborhoods $M_{P_{x_i^2}}$ and aligned with respect to the minutia orientation $\theta_i(V_{\eta})$. Following this, the selected segment of the minutia region is resized as:

$$\varphi_{(c_i)} = \sqrt{2P_{(p,q)}V_{\eta}}.$$

**Phase Patch Segmentation and Normalization.** Phase patch segmentation uses the extracted local patch for foreground area selection. This drastically reduces the execution time of the convolutional neural network because only the discriminative part that is rich in information is considered and used in training. The reduction in the amount of input information reduces the model size (number of parameters). This ensures that valid fingerprint information contained in the segmented patch is fully retained by the network architecture and is adequate for classification.

Figure 2 shows the steps involved in local phase patch segment extraction. Before training the segmented local patches using the convolutional neural network, the patches undergo a series of data enhancement processes that perform normalization to eliminate inconsistencies that may have been introduced during segmentation. Following this, data augmentation is performed to increase the number of samples required for training; this also helps get rid of overfitting.

**FinSpoofNet Architecture.** Several fingerprint liveness detection techniques such as VGG19 [25], FeatherNets [32], SqueezeNet [27] and
MobileNetV2 [28] use transfer learning techniques to fine-tune convolutional neural networks. A major drawback with fine-tuning a convolutional neural network architecture is that the final size and number of layers of the trained model are too large. This is because the original design is not intended for fingerprint images, which contain different levels of condensed information compared with digital images. As a result, the architecture cannot be implemented on low-end devices such as mobile phones; additionally, it has weak generalization capability. In contrast, the lightweight FinSpoofNet architecture developed in this research is designed from scratch with convolution, pooling and dense layers (called triple-dense). It can be implemented in lightweight mobile device applications and exhibits high spoofing detection efficiency.

Inspired by the advantages of DenseNets [13, 15], such as feature reuse, feature propagation strength and substantial reduction in the number of parameters, the last two blocks of the FinSpoofNet architecture incorporate three layers of dense networks. This is followed by attention-pooling in the fourth block before the final softmax function. The input size is decreased in each initial convolution layer while keeping the kernel size constant. All these layers incorporate the ReLU6 activation function, except for the last layer that uses the sigmoid activation function.

Conventional practice is to apply drop-out exclusively in the dense layers. However, in the FinSpoofNet architecture, drop-out is also applied in the convolution layers because the overall accuracy is increased significantly. Moreover, because overfitting has been shown to occur most commonly in the preceding network layers, a dropout value of 0.5 was adopted for the convolution layers and a dropout value of 0.2 was adopted for the equivalent dense layers in the third and fourth blocks.

Input image sizes of 112 × 112 × 3 pixels and 64 × 64 × 3 pixels were tested on different training sets to determine the optimum size for spoofed fingerprint detection. Eventually, a decision was made to use 112 × 112 × 3 pixels, which were subsequently reduced in terms of convolutions. A 112-size 3 × 3 kernel with stride 1 was used on each input, yielding 64 × 64 feature maps of size 16. Max-pooling with 3 × 3 kernel size and stride 2 was then used to filter the maximum activations from the preceding 64 feature maps. In addition, a convolutional layer of filter size 3 × 3 and average-pooling with stride 1 was used, yielding 28 × 16 × 16 feature maps. Another average-pooling with kernel size 3 × 3 and stride 1 was then applied to help filter less important discriminative features. Finally, the softmax function was applied at the output step to recognize the two classes (live and spoofed fingerprints) according to the classification problem.
<figure><img src="image_5.png" /><figcaption>Figure 3. FinSpoofNet architecture.</figcaption></figure>

In summary, after the first two-dimensional convolution layer, four major blocks were applied, each block constituting a transition layer with a 1 × 1 conv in the first three blocks. This was followed by max-pooling, average-pooling, average-pooling and attention-pooling layers with strides 1, 2, 1 and 1, respectively. To ensure robust and discriminative information extraction from the network, [4 × 4 conv] × 3 conv filters were incorporated in all the blocks. This ensured the retention of important information previously extracted by local phase quantization (via local phase patch segment extraction). The final classification layer constituting attention-pooling and softmax assisted with the live and spoofed categorization of input patches. Figure 3 shows the FinSpoofNet architecture. Table 1 provides the detailed specification of the FinSpoofNet architecture.

The ReLU6 activation function was chosen to obtain superior performance in terms of fast processing capability, quantization friendliness due to the elimination of precision loss caused by various approximations and, of course, nonlinearity reduction. ReLU6 was used in all the layers of the FinSpoofNet architecture, except for the first and last layers. This helps maintain the lightness of the architecture. Additionally, all the features were adequately retained without a drop in resolution.

# 4. Experimental Results and Evaluation

This section presents an in-depth evaluation of the proposed fingerprint liveness detection methodology. First, the evaluation metrics are specified, and the datasets and training and testing strategies used in the experiments are described. Finally, the performance of the proposed methodology is compared against the performance of several state-of-the-art fingerprint liveness detection techniques.
<table><caption>Table 1. Tabular representation of the FinSpoofNet architecture.</caption><thead><tr><th>Layers</th><th>Output</th><th>FinSpoofNet</th><th>Stride</th><th>Parameters</th></tr></thead><tbody><tr><td>Input Image</td><td>112 × 112</td><td>32 × 32 × 1</td><td></td><td></td></tr><tr><td>Convolution</td><td>112 × 112</td><td>32 × conv</td><td>1</td><td>55,405</td></tr><tr><td>Block 1</td><td>64 × 64</td><td>[4 × 4 conv] × 3</td><td></td><td>86,625</td></tr><tr><td>Transition</td><td>64 × 64</td><td>1 × 1 conv</td><td></td><td>24,353</td></tr><tr><td>Layer 1</td><td>32 × 32</td><td>3 × 3 max-pooling</td><td>2</td><td></td></tr><tr><td>Block 2</td><td>28 × 28</td><td>[4 × 4 conv] × 3</td><td></td><td>82,284</td></tr><tr><td>Transition</td><td>28 × 28</td><td>1 × 1 conv</td><td></td><td>27,192</td></tr><tr><td>Layer 2</td><td>16 × 16</td><td>32 × 2 average-pooling</td><td>1</td><td></td></tr><tr><td>Block 3</td><td>8 × 8</td><td>[4 × 4 conv] × 3</td><td></td><td>80,788</td></tr><tr><td>Transition</td><td>8 × 8</td><td>1 × 1 conv</td><td></td><td>24,557</td></tr><tr><td>Layer 3</td><td>4 × 4</td><td>2 × 2 average-pooling</td><td>1</td><td></td></tr><tr><td>Block 4</td><td>2 × 2</td><td>[4 × 4 conv] × 3</td><td></td><td>71,305</td></tr><tr><td>Classification</td><td>1 × 1</td><td>3 × 3 attention-pooling</td><td>1</td><td></td></tr><tr><td>Layer</td><td></td><td>Softmax</td><td></td><td></td></tr><tr><td colspan="3">Total Parameters</td><td></td><td>452,509</td></tr></tbody></table>

## 4.1 Evaluation Metrics

The performance of the proposed methodology is primarily evaluated using the average classification error (ACE) metric, which is recommended as the standard evaluation metric in the Fingerprint Liveness Detection (LivDet) Competitions [8]. The ACE metric is computed as:

$$ACE = \frac{\text{FMR}(\text{Ferrlive}) + \text{FNMR}(\text{Ferrfake})}{2}$$

where the FMR(Ferrlive) denotes the total average false match rate of misclassified live fingerprint images and FNMR(Ferrfake) denotes the total average false non-match rate of misclassified spoofed fingerprint images.

FMR(Ferrlive) and FNMR(Ferrfake) are computed as:

$$\text{FMR}(\text{Ferrlive}) = \frac{\text{Number of misclassified live images}}{\text{Number of live images}}$$

$$\text{FNMR}(\text{Ferrfake}) = \frac{\text{Number of misclassified spoofed images}}{\text{Number of spoofed images}}$$

For better performance, ACE is generally represented as a small value with respect to the pre-defined threshold value of $\gamma = 0.5$. Images are
classified as live fingerprints when $\gamma > 0.5$ and spoofed fingerprints when $\gamma \le 0.5$.

## 4.2 Datasets

The publicly-available LivDet 2013 [9] and LivDet 2015 [21] datasets were employed to evaluate the performance of the proposed fingerprint liveness detection methodology. The spoofed fingerprint images in the two datasets were collected from fabricated fingerprints created with materials such as Ecoflex, gelatin, latex, Modasil and wood glue.

The LivDet 2013 dataset contains fingerprint images collected using Biometrika and Italdata fingerprint readers for training purposes, and images collected from Biometrika, Crossmatch, Italdata and SwipeTest fingerprint readers for testing purposes. The experimental evaluations only employed the Biometrika and Italdata data for training and testing. This is because anomalies discovered in Crossmatch readers discourage their use for evaluation purposes [8] and SwipeTest readers have relatively low resolutions of about 96 dpi compared with other readers. The Biometrika and Italdata images in the LivDet 2013 have sizes of 315×372 pixels and 640 × 480 pixels with resolutions of 569 dpi and 500 dpi, respectively. The training/testing image splits were 1,000/1,000 for live and spoofed Biometrika and Italdata images; the images were selected without subject cooperation.

The LivDet 2015 dataset contains fingerprint images collected using Biometrika, Digital Persona, Green Bit and Crossmatch fingerprint readers. The images have resolutions of 500 dpi, except for the Biometrika images that have 1,000 dpi resolutions. The training/testing splits were 1,000/1,000 for live images and 1,000/1,500 for spoofed images for all the fingerprint readers except Crossmatch, whose training/testing splits were 1,510/1,473 for live images and 1,500/1,448 for spoofed images. All the images were selected with subject cooperation. The spoofed images collected using the Biometrika, Digital Persona and Green Bit fingerprint readers employed two additional materials, Liquid Ecoflex and RTV, than those used to create the LivDet 2013 dataset. The Crossmatch spoofed images were collected from fabricated fingerprints created using Body Double, Ecoflex, Play-Doh, OOMOO and gelatin materials.

Figure 4 shows sample live and spoofed fingerprint images from the LivDet 2013 and LivDet 2015 datasets. Figures 4(a) and 4(b) show sample LivDet 2013 images taken by Biometrika and Italdata fingerprint readers, respectively. Figures 4(c) and 4(d) show sample LivDet 2015 images taken by Digital Persona and Green Bit fingerprint readers, respectively.
<figure><img src="image_6.png" /><figcaption>(a) Sample LiveDet 2013 images captured by Biometrika fingerprint readers.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(b) Sample LiveDet 2013 images captured by Italdata fingerprint readers.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(c) Sample LiveDet 2015 images captured by Digital Persona fingerprint readers.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(d) Sample LiveDet 2015 images captured by Green Bit fingerprint readers.</figcaption></figure>

**Figure 4.** Fingerprint images from the LiveDet 2013 and LiveDet 2015 datasets.

Table 2 presents a comprehensive description of the LivDet 2013 and LivDet 2015 datasets used in the experimental evaluations. Note that the datasets contain images with different sizes (column 4). The image size diversity makes it imperative to apply the patch segment extraction to create input images with fixed (common) size. This eases convolutional neural network adaptation during the training phase, contributing to efficiency as well as improved accuracy.
<table><caption>Table 2. Summary of the LiveDet 2013 and LiveDet 2015 datasets used in the experimental evaluations.</caption><thead><tr><th>Dataset</th><th>Fingerprint Reader</th><th>Model</th><th>Image Size, Resolution (dpi)</th><th>Cooperative Subject</th><th>Live/Spoofed Images (Training/Testing)</th><th>Spoofing Materials</th></tr></thead><tbody><tr><td rowspan="2">LiveDet 2013 [9]</td><td>Biometrika</td><td>FX2000</td><td>315 × 372, 569</td><td>No</td><td>1,000/1,000, 1,000/1,000</td><td>Ecoflex, Gelatin, Latex, Modasil, Wood Glue</td></tr><tr><td>Italdata</td><td>ET10</td><td>640 × 480, 500</td><td>No</td><td>1,000/1,000, 1,000/1,000</td><td></td></tr><tr><td rowspan="5">LiveDet 2015 [21]</td><td>Biometrika</td><td>HiScan-PRO</td><td>1000 × 1000, 1000</td><td>Yes</td><td>1,000/1,000, 1,000/1,500</td><td>Ecoflex, Gelatin, Latex, Wood Glue, Liquid Ecoflex, RTV</td></tr><tr><td>Digital</td><td>U.are.U 5160</td><td>252 × 324, 500</td><td>Yes</td><td>1,000/1,000, 1,000/1,500</td><td></td></tr><tr><td>Persona</td><td>DactyScan26</td><td>500 × 500, 500</td><td>Yes</td><td>1,000/1,000, 1,000/1,500</td><td></td></tr><tr><td>Green Bit</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Crossmatch</td><td>L Scan Guardian</td><td>640 × 480, 500</td><td>Yes</td><td>1,510/1,473, 1,500/1,448</td><td>Body Double, Ecoflex, Play-Doh, OOMOO, Gelatin</td></tr></tbody></table>
## 4.3 Performance Evaluation

The performance evaluation examined the classification accuracy of the proposed fingerprint liveness detection methodology for each training set and validation set drawn from the LivDet 2013 and LivDet 2015 datasets.

**Robustness Comparison.** Three fingerprint liveness detection scenarios were developed to investigate the robustness of the proposed methodology against unknown spoofing materials and different sensors. The three scenarios are: (i) known materials and known sensors, (ii) unknown materials and known sensors and (iii) known materials and unknown sensors.

**Known Materials and Known Sensors Scenario.** In the first evaluation scenario, the materials used to create spoofed fingerprints and sensors used to capture the fingerprint images were known, i.e., they were used for training and testing. The spoofed fingerprint materials were mainly used for testing purposes whereas all the captured fingerprint images (live and spoofed) were utilized for training and testing purposes. The scenario was designed to evaluate the robustness of the proposed methodology against other light architectures for detecting spoofed fingerprints, specifically, Slim-ResCNN [34], fPADnet [23], TripleNet [26] FDLNet [33] and DRN [31]. To ensure inclusion, FinSpoofNet is also evaluated against traditional software (non-deep learning) techniques, specifically, WLBD [29], FPAD [10] and LCPD [11].

Table 3 shows that the proposed FinSpoofNet methodology generally outperforms most of the state-of-the-art techniques with average ACE metric values of 0.31% and 2.26% for the LivDet 2013 and LivDet 2015 datasets, respectively. In contrast, Slim-ResCNN, fPADnet and LCPD yield average ACE metric values of 1.74%, 1.10% and 1.25%, respectively, for the LivDet 2013 dataset. Also, Slim-ResCNN, fPADnet and FPAD yield average ACE metric values of 3.11%, 3.58% and 2.82%, respectively, for the LivDet 2015 dataset. The proposed FinSpoofNet methodology yields the best overall average ACE metric value of 1.62% whereas WLBD has the worst overall average ACE metric value of 6.68%.

**Unknown Materials and Known Sensors Scenario.** In the second evaluation scenario, the materials used to create spoofed fingerprints for testing were not used for training, but the sensors used to capture the fingerprint images were used for training and testing. Robustness was evaluated using the LivDet 2015 dataset in a cross-material fashion
<table><caption>Table 3. ACE comparison against lightweight deep-learning techniques and traditional software techniques.</caption><thead><tr><th rowspan="2">Technique</th><th colspan="2">LivDet 2013</th><th colspan="2">LivDet 2015</th><th rowspan="2">Overall Avg.</th></tr><tr><th>Biometrika</th><th>Italdata</th><th>Avg.</th><th>Biometrika</th><th>Digital Person</th><th>Green Bit</th></tr></thead><tbody><tr><td colspan="7"><strong>Deep-Learning-Based</strong></td></tr><tr><td>Slim-ResCNN [34]</td><td>0.47</td><td>3.01</td><td>1.74</td><td>3.10</td><td>2.37</td><td>2.64</td><td>4.32</td><td>3.11</td><td>2.65</td></tr><tr><td>fPADnet [23]</td><td>0.90</td><td>1.30</td><td>1.10</td><td>4.10</td><td>8.50</td><td>1.40</td><td>0.30</td><td>3.58</td><td>2.75</td></tr><tr><td>TripleNet [26]</td><td>0.65</td><td>0.50</td><td>0.56</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>FLDNet [33]</td><td>0.36</td><td>1.35</td><td>0.86</td><td>2.95</td><td>3.61</td><td>0.53</td><td>1.78</td><td>2.22</td><td>1.76</td></tr><tr><td>DRN [31]</td><td>2.00</td><td>1.80</td><td>1.90</td><td>6.24</td><td>6.80</td><td>4.77</td><td>3.46</td><td>5.32</td><td>4.18</td></tr><tr><td colspan="7"><strong>Software-Based</strong></td></tr><tr><td>WLBD [29]</td><td>0.40</td><td>0.95</td><td>0.68</td><td>9.64</td><td>13.72</td><td>4.53</td><td>10.82</td><td>9.68</td><td>6.68</td></tr><tr><td>FPAD [10]</td><td>0.50</td><td>0.10</td><td>0.30</td><td>3.20</td><td>4.60</td><td>1.20</td><td>2.28</td><td>2.82</td><td>1.98</td></tr><tr><td>LCPD [11]</td><td>1.20</td><td>1.30</td><td>1.25</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>FinSpoofNet</td><td>0.38</td><td>0.27</td><td>0.31</td><td>2.88</td><td>3.42</td><td>1.35</td><td>1.41</td><td>2.26</td><td>1.62</td></tr></tbody></table>
<table><caption>Table 4. Robustness comparison against the LivDet 2015 and LivDet 2017 winners.</caption><thead><tr><th rowspan="2">Technique</th><th rowspan="2">LivDet 2015 Dataset</th><th colspan="4">Unknown Materials and Known Sensors</th></tr><tr><th>Ferrlive (%)</th><th>Ferrfake (%)</th><th>Ferrfake-unknown (%)</th><th>Ferrfake-known (%)</th></tr></thead><tbody><tr><td>Winner LivDet 2015 [21]</td><td>Biometrika</td><td>8.50</td><td>3.73</td><td>2.70</td><td>5.80</td><td>5.64</td></tr><tr><td></td><td>Digital Persona</td><td>8.10</td><td>5.07</td><td>4.60</td><td>6.00</td><td>6.28</td></tr><tr><td></td><td>Green Bit</td><td>3.50</td><td>5.33</td><td>4.30</td><td>7.40</td><td>4.60</td></tr><tr><td></td><td>Crossmatch</td><td>0.93</td><td>2.90</td><td>2.12</td><td>4.02</td><td>1.90</td></tr><tr><td></td><td><strong>Average</strong></td><td>4.78</td><td>4.27</td><td>3.48</td><td>5.72</td><td>4.49</td></tr><tr><td>Winner LivDet 2017 [22]</td><td>Biometrika</td><td>3.35</td><td>4.23</td><td>2.44</td><td>7.72</td><td>2.98</td></tr><tr><td></td><td>Digital Persona</td><td>4.28</td><td>4.78</td><td>3.73</td><td>6.40</td><td>4.58</td></tr><tr><td></td><td>Green Bit</td><td>2.22</td><td>2.65</td><td>2.42</td><td>3.11</td><td>2.19</td></tr><tr><td></td><td>Crossmatch</td><td>1.72</td><td>3.91</td><td>3.18</td><td>4.58</td><td>2.99</td></tr><tr><td></td><td><strong>Average</strong></td><td>3.14</td><td>3.89</td><td>2.99</td><td>5.45</td><td>3.18</td></tr><tr><td>FinSpoofNet</td><td>Digital Persona</td><td>3.75</td><td>2.94</td><td>2.55</td><td>4.83</td><td>3.42</td></tr><tr><td></td><td>Green Bit</td><td>1.36</td><td>1.56</td><td>1.39</td><td>1.95</td><td>1.35</td></tr><tr><td></td><td>Biometrika</td><td>2.04</td><td>3.88</td><td>2.42</td><td>6.01</td><td>2.88</td></tr><tr><td></td><td>Crossmatch</td><td>1.13</td><td>2.74</td><td>1.97</td><td>4.25</td><td>1.41</td></tr><tr><td></td><td><strong>Average</strong></td><td>2.07</td><td>2.78</td><td>2.08</td><td>4.26</td><td>2.26</td></tr></tbody></table>

because the LivDet 2015 testing dataset comprises spoofed fingerprint images created using six unknown materials.

Table 4 compares the robustness of the proposed FinSpoofNet methodology against the LivDet 2015 [21] and LivDet 2017 [22] competition winners. Note that the percentages of misclassified spoofed images created using known and unknown materials are identified as Ferrfakeknown and Ferrfakeunknown, respectively. Also, Ferrfake denotes the misclassified spoofed fingerprints under the number of samples in the Ferrfakeknown and Ferrfakeunknown categories.

Table 4 shows that the proposed FinSpoofNet methodology generally outperforms the other techniques in terms of the ACE metric values. However, the LivDet 2015 winner yields slightly better robustness for Crossmatch images in the Ferrlive and Ferrfakeknown categories.

To ensure further generalization capability, the LivDet 2013 dataset was used for a second set of cross-material tests while adopting the protocol employed by Nogueira et al. [25]. Table 5 presents the results of the comparison. The proposed FinSpoofNet methodology has a lower error rate than the FSB [3], TripleNet [26] and VGG19 [25] techniques, demonstrating that FinSpoofNet has the best efficiency and robustness.
<table><caption>Table 5. Cross-material robustness comparison against state-of-the-art techniques.</caption><thead><tr><th>Dataset</th><th>Training Materials</th><th>Testing Materials</th><th>FSB [3]</th><th>TripleNet [26]</th><th>VGG19 [25]</th><th>FinSpoofNet</th></tr></thead><tbody><tr><td>Biometrika</td><td>Wood Glue,</td><td>Modasil</td><td>1.30</td><td>2.10</td><td>4.90</td><td>0.75</td></tr><tr><td>Italdata</td><td>Ecoflex</td><td>Gelatin, Latex</td><td>0.60</td><td>1.25</td><td>6.30</td><td>1.22</td></tr><tr><td>Average</td><td></td><td></td><td>0.95</td><td>1.68</td><td>5.60</td><td>0.98</td></tr></tbody></table>

<table><caption>Table 6. Cross-sensor comparison against state-of-the-art techniques.</caption><thead><tr><th>Training Dataset (Testing Dataset)</th><th>FSB [3]</th><th>TripleNet [26]</th><th>FLDNet [33]</th><th>FinSpoofNet</th></tr></thead><tbody><tr><td>Biometrika (Italdata)</td><td>4.30</td><td>1.55</td><td>2.10</td><td>1.96</td></tr><tr><td>Italdata (Biometrika)</td><td>3.50</td><td>3.80</td><td>2.90</td><td>2.98</td></tr><tr><td>Average</td><td>3.90</td><td>2.68</td><td>2.50</td><td>2.47</td></tr></tbody></table>

**Known Materials and Unknown Sensors Scenario.** In the third evaluation scenario, the materials used to create spoofed fingerprints were used for training and testing. However, different sensors were employed to capture fingerprint images in the training and testing sets. This scenario primarily evaluates the discrimination ability of fingerprint liveness detection techniques under environmental and sensor variations. The protocol used in the second scenario [25] was adopted to select the training and testing sets. Table 6 shows that the proposed FinSpoofNet methodology has better discrimination capabilities than the FSB [3], TripleNet [26] and FLDNet [33] techniques.

## 4.4 System Setup and Processing Time

The experiments employed a MATLAB VerR2018b implementation with a Corei7-3.00 GHz processor and 16 GB RAM for local phase patch segment extraction and a Keras deep learning framework with a GTX 1080 GPU to implement the FinSpoofNet architecture and perform evaluations. The processing time for a single batch of 50 patches in the FinSpoofNet methodology was about 0.8 ms, which outperformed most of the state-of-the-art techniques. Thus, the proposed FinSpoofNet methodology meets the real-time processing requirements of the best-performing fingerprint liveness detection techniques.
# 5. Conclusions

The novel fingerprint liveness detection methodology developed in this research incorporates local phase patch segment extraction (coined from local phase quantization) and a custom lightweight convolutional neural network architecture. Local phase patch segment extraction ensures local patch segmentation and the extraction of minutiae and local texture features from fingerprint images, which are subsequently trained and validated by the convolutional neural network. This leads to the extraction of additional discriminative and semantic feature information, following which the final softmax classifier differentiates between live and spoofed fingerprint images. The lightweight FinSpoofNet architecture enables fingerprint liveness detection to be deployed in real-time applications on devices with limited computational resources.

Experimental evaluations involving comparisons against state-of-the-art deep learning and software-based techniques demonstrate that the proposed fingerprint liveness detection methodology has superior classification accuracy. Additionally, the methodology demonstrates good robustness and discrimination characteristics in three key scenarios involving known materials and known sensors, unknown materials and known sensors, and known materials and unknown sensors. The experimental results also demonstrate the overall efficiency of the fingerprint liveness detection methodology.

Future work will attempt to incorporate a matching algorithm in the convolutional neural network architecture using a fusion approach. This will provide improved generalization and robustness characteristics when identifying spoofed fingerprints created using unknown materials.

# Acknowledgement

This research was supported by China Three Gorges University High-Level Talent Funding under Grant No. 1910103.

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