Face/Fingerphoto Spoof Detection under Noisy Conditions by using

Deep Convolutional Neural Network

Masakazu Fujio

, Yosuke Kaga

, Takao Murakami

, Tetsushi Ohki

and Kenta Takahashi

Security Research Dept., Hitachi, Ltd., Tokyo, Japan

Advanced Cryptosystems Research Group,

National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

Department of Computing, Shizuoka University, Shizuoka City, Japan

Keywords: Biometrics, Spoofing, LBP, CNN, Deep Learning, Mobile, Blurriness.

Abstract: Most of the generic camera based biometrics systems, such as face recognition systems, are vulnerable to

print/photo attacks. Spoof detection, which is to discriminate between live biometric information and attacks,

has received increasing attentions recently. However, almost all the previous studies have not concerned the

influence of the image distortion caused by the camera defocus or hand movements during image capturing. In

this research, we first investigate local texture based anti-spoofing methods including existing popular

methods (but changing some of the parameters) by using publicly available spoofed face/finger photo/video

databases. Secondly, we investigate the spoof detection under the camera defocus or hand movements during

image capturing. To simulate image distortion caused by camera defocus or hand movements, we create

blurred test images by applying image filters (Gaussian blur or motion blur filters) to the test datasets. Our

experimental results demonstrate that modifications of the existing methods (LBP, LPQ, DCNN) or the

parameter tuning can achieve less than 1/10 of HTER（half total error rate）compared to the existing results.

Among the investigated methods, the DCNN (AlexNet) can achieve the stable accuracy under the increasing

intensity of the blurring noises.

1 INTRODUCTION

With the exponential growth of the smartphone

market, financial services are also accelerated by the

development of various services on mobile devices,

such as mobile payments, money transfer, and all

banking related transactions.

As the mobile e-commerce continues to grow, the

biometrics authentications are attracting more

attentions for the secure and easy-to-use

authentication methods, as the alternative for the

insecure and inconvenient Password/PIN

authentication methods.

Biometrics can implement the convenient user

authentication, but on the other hand, it is vulnerable

to be spoofed by the fake copies of the user biometric

features made of commonly available materials such

as clay and gelatines. For example, the gummy finger

model attacks for the fingerprint biometrics solutions

demonstrate the possibilities of the unauthorized

accesses. Especially the generic camera based

biometrics has the higher risk of spoofing by the

printed photos and videos (preparation costs are low).

For that reason, the spoof detection, which is to

discriminate between live faces/fingers and attacks,

has received increasing attentions recently

(Keyurkumar et al., 2015; Bai et al., 2010).

Typical anti-spoofing techniques can be coarsely

classified into three categories based on clues used for

spoof attack detection: (i) motion analysis based

method, (ii) texture analysis based methods, and (iii)

image qualities analysis based methods.

(i) Motion analysis based methods

These methods, which are effective to counter

printed photo attacks, capture the movement clues

such as eye blinks (Gang et al., 2007) and lip

movements (Avinash et al., 2014), which are very

important cues for vitality. But in the case of the face

biometrics, the system needs accurate detections of

facial parts such as eyes, lips and so on. Furthermore,

Fujio, M., Kaga, Y., Murakami, T., Ohki, T. and Takahashi, K.

Face/Fingerphoto Spoof Detection under Noisy Conditions by using Deep Convolutional Neural Network.

DOI: 10.5220/0006597500540062

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS, pages 54-62

ISBN: 978-989-758-279-0

simply capturing movement clues are not enough for

the presentation attacks by videos.

(ii) Texture analysis based methods

These methods capture the texture features

appeared on natural scenes, photo papers, and

displays under the assumption that surface properties

of real faces and prints are different (Diego and

Giovanni, 2015; Tiago, 2012, 2014; Ivana, 2012; Juho

et al., 2012).

Texture based methods such as Local Binary

Patterns (LBP) (Matti et al., 2011) have achieved

significant success on the Idiap and CASIA databases

(Ivana et al., 2012; Zhiwei et al., 2012). For example,

The Half Total Error Rate (HTER) on the Idiap

database was reduced from 13.87% in (Ivana et al.,

2012) to 6.62% in (Samarth, 2013). Unlike motion

based methods, texture based methods need only a

single image to detect a spoofing.

Other types of texture analysis methods adopt the

frequency domain features (Jiangwei et al., 2004). For

example, low resolution printed images have a high-

frequency spectral magnitude in the frequency

domain, caused by periodic dot printing (Xiaofu et al.,

2009). Jiangwei et al. (2004) described a method for

detecting print-attack face spoofing by exploiting

differences in the 2D Fourier spectra of live and

spoofed images. The method assumes that

photographs are normally smaller in size and contain

less high-frequency components compared to real

faces. Then their method likely fails for higher-

quality samples.

(iii) Image qualities analysis based methods

These methods capture the degradation of the

image qualities caused by presenting photographs or

videos to the generic cameras (Javier et al., 2014;

Diogo and Ricardo, 2015; Di et al., 2015). For

example, printed/displayed images usually have

lower resolution, narrow dynamic range, specular

reflection, reduced image contrast and defocused

blurriness. But those image quality degradations also

appear in both genuine and spoofed face images, it is

not simple to distinguish that the image distortions are

caused by spoofing or camera operations.

The problems of the almost all the existing studies

are that they have not concerned the influence of the

image distortion caused by the camera defocus or

hand movements during an image capturing.

In this research, we first investigate local texture

based anti-spoofing methods including existing

popular methods (but changing some of the

parameters) by using publicly available spoofed

face/fingerphoto/video databases (Replay-Attack

Database and Spoofed Fingerphoto Database).

Secondly, we investigate the spoof detection under

the camera defocus or hand movements during image

capturing. To simulate image distortion caused by

camera defocus or hand movements, we create

blurred images by applying image filters (Gaussian

blur or motion blur filters) to the test datasets. For the

training images, we do not apply image filters.

The remaining of the paper is organized as follows.

Section 2 contains the examined schemes of the anti-

spoof technique and provides an explanation of the

countermeasures to photo/display attacks in

fingerphoto or face recognition. Section 3 details

experimental protocols, the dataset statistics,

parameters used in the algorithm and the results

obtained. Section 4 concludes the paper.

2 PROPOSED METHODS

To design the anti-spoofing countermeasures, we

investigated fingerphoto spoofed image database

(Archit et al., 2016). Fig. 1 shows magnified images

(The left side is a genuine image, and the right side is

a spoofed image.). From the Fig. 1, we can see block

noises in the spoofed images.

(a) genuine (b) spoofed

Figure 1: Magnified fingerphoto images.

To highlights the noises in the images, we

performed image enhancement of the above images

based on wavelet transform (Fig. 2). From the Fig. 2,

we can see repeated block noise artifacts in the

spoofed images (The square frame in the left image

in the fig. 2 shows a block of 8x8 pixels).

(a) genuine (b) spoofed

Figure 2: Wavelet transformed images (left：genuine, right

：spoofed).

To capture the noise features found in the

preliminary analysis, we focus on the LBP (Matti et

al., 2011) and the block noise indicator (Zhou et al.,

Face/Fingerphoto Spoof Detection under Noisy Conditions by using Deep Convolutional Neural Network

2000, 2002). The block noise indicator is used to

quantify the magnitude of block artifacts caused by

the application of lossy compression algorithms such

as JPEG compression. And used for the judgment of

the image compression algorithms (Zhou et al., 2000,

2002). In the rest of this section, we will explain anti-

spoofing techniques based on handcrafted features or

the automatic feature extraction based on CNN

(convolutional neural network).

2.1 SVM with Handcrafted Features

We used the following 3 handcrafted feature vectors

to train Support Vector Machine (SVM) classifiers.

(1) WLBP (Wavelet transformed Local Binary

Pattern)

LBP (Local Binary Patterns) is the 8-bit encoding

based on the comparisons of the magnitudes of the

luminance between the focused pixel and neighboring

8 pixels. LBP is widely used for the image

classification tasks in the literature.

Figure 3: LBP features of the each pixel.

In addition to the LBP of original image size, we

extracted LBP features from compressed images and

contrast enhanced images. All features are

concatenated and used for the training of the linear

SVM.

(a) Calculation of LBP from the original images:

(b) Image enhancement (wavelet transformation):

To enhance the noise patterns in the images,

perform wavelet transformations and calculate LBP

of the transformed images

Base on the preliminary study about block noise

pattern, we set the compression ratio as 3/8 (“3” is the

kernel size of LBP, and “8” is the size of the observed

blockiness).

(d) Feature fusion:

Concatenate the three LBP (original, wavelet

transformed, compressed), and use for the training of

the linear SVM

(2) NRPQA (No-Reference Perceptual Quality

Assessment)

This measure applies the block artifact indicator

(2002) for the ant-spoof detections. Zhou et al. (2002)

describes perceptual quality assessment of JPEG

compressed images by calculating three measures,

inter block differential, intra block differential and

zero-crossing rate.

Figure 4: 3 types of LBP features.

We denote the test image signal as x (m; n) for

 

Mm ,1

and

 

Nn ,1

, and calculate a differential

signal along each horizontal line:

(1)

The features are calculated horizontally and then

vertically. The amount of blockiness is estimated as

the average differences across boundaries.

 

 











jid

18/

)8,(

)18/(

(2)

Second, we estimate the activity of the image

signal. The second activity measure, is the average

absolute difference between in-block image pixels.

























Bjid

),(

)1(

(3)

The third activity measure , is the zero-crossing

(ZC) rate. Horizontal Zero-Crossing (ZC) means that

there is a change of the sign of the value

),( nmd

between n and n+1 (

 

　2,1  Nn









otherwise

nmdatZChorizontal

nmz

),(1

),(

(4)

ZC is then estimated by using

),( nmZ

as follows:













nmz

),(

)2(

(5)

For the detail of these measures, please refer to

(Zhou et al., 2002). In this study, we trained linear-

SVM with these 6 features {

hhhhhh

ZABZAB ,,,

(3) LPQ (Local Phase Quantization)

The local phase quantization (LPQ) (Timo, 2008) is a

method based on the blur invariance property of the

Fourier phase spectrum and uses the local phase

information extracted using 2-D discrete Fourier

transform or short term Fourier transform (STFT),

computed over a rectangular region. The STFT over

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

a region of the N by N neighborhood

of image

g (x) with each position of the pixel x is defined by

uTyj

hweyxhxuH











)( ),(

(6)

where is the basis vector of the 2D discrete

Fourier transform at a frequency

while

stands

for the vector containing all

pixels.

In the task of spoofing detection, we expect that

LPQ is tolerant for the distorted images caused by

defocused images or motion blurred images, which

are commonly seen in both printed photos, replayed

video attacks and real faces/fingers.

2.2 Deep Convolutional Neural

Network (DCNN)

Convolutional Neural Networks (Alex et al., 2012)

have demonstrated state-of the-art performances in a

variety of image recognition benchmarks, such as

MNIST (Yann and Corinna., 2010), CIFAR-10,

CIFAR-100 (Alex and Geoffrey, 2009), and ImageNet

(Alex et al., 2012).

In this study, we used AlexNet (Alex et al., 2012).

This model won both classification and localization

tasks in the ILSVRC-2012 competition. This model

also exhibited good results on the spoof detection on

the Replay-Attack Database (HTER<0.5%) (Koichi

et al., 2017).

• CNN Model： AlexNet without pre-trained

weights.

As the preliminary experiments, we tried various

sizes of the image compressions and cropping sizes

for the training of the DCNN (AlexNet) models.

Based on the results of the preliminary experiments,

we choose the combination of image resizing:256

pixels and image cropping size:227 pixels, which

exhibited the highest accuracy.

We set the parameters for the training of CNN as

follows:

Output layer number: 2, Resized image size: 256,

random cropping size: 227, batch size: 100, epoch

size: 300,000, learning rate: 0.01, weight decay:

0.004.

In the above settings, we do not use image

augmentation (except for the random image

cropping). We consider that not only foreground

regions but also of images (such as faces and finger)

but also both background images and foreground

images have discriminative cues for the spoof

detection.

3 EXPERIMENTS

In this section, we first provide an overview of the

datasets in our experiments, and present our initial

results for the proposed methods in previous section

(one is linear-SVM with the handcrafted feature

vectors, and the other is Deep Convolutional Neural

Network (DCNN)).

3.1 Database Descriptions

To evaluate the spoofing detection accuracies, we

used two databases, one is “Replay Attack Database

(face) and the other is “Spoofed Fingerphoto

Database (finger)”.

A) Spoofed Fingerphoto DB

Table 1: Summary of the Spoofed Fingerphoto DB.

Condition

Description

Lighting

condition

2types (Indoor/Outdoor）

Background

2types（White/Natural）

Image size

Genuine image：3264x2448

Spoofed image：2332x1132

Number of

images

Genuine image: 4096（64x2x8x4）

Spoofed image: 8192（64x2x2x8x4）

Table 2: Attack Protocols for Fingerphoto Spoofing.

Capture

Display

OnePlus One phone

iPad

OnePlus One phone

Laptop

OnePlus One phone

Nexus

OnePlus One phone

Printout

Nokia

iPad

Nokia

Laptop

Nokia

Nexus

Nokia

Printout

Following the setup of Tanja (2016), genuine

images (4096) were split into the gallery and probe

data (each has 2048 images). Then the images used

for generating spoof images (512) were excluded

from genuine images (1536), and spoofed images

were added (4096) to the training of the each model

(total 5632).

From probe data, genuine images (2048) +

imposter images (4096) were used for the evaluation

data set.

B) Replay-Attack DB

The 2D face spoofing attack database consists of

1,300 video clips of photo and video attack attempts

of 50 clients, under different lighting conditions. The

size of the image is 320 x240.

Face/Fingerphoto Spoof Detection under Noisy Conditions by using Deep Convolutional Neural Network

Table 3: Attack protocols for face spoofing.

Setting

Description

Capture

condition

Lighting

condition

2types (controlled/adverse）

Attack

protocols

Display

devices

5types

(mobile photo/mobile video/high-

resolution photo/high-resolution

video/high-resolution print)

Attack modes

2types (hand/fixed)

Training data size:

Attack Video: 300 clips (15 (clients) × 2

(lighting) × 5 (devices) × 2 (modes) )

Real Video: 60 clips (15 (clients) × 2 (lighting)

× 2 (shots))

Test data size:

Attack Video: 400 clips (20 (clients) × 2

(lighting) × 5 (devices) × 2 (modes))

Real Video: 80 clips (20 (clients) × 2 (lighting)

× 2 (shots))

Following the setup of Ito (2017), we used the

“training set” for the training, and evaluated the

spoofing detection accuracies with the “test set”. To

train the face spoof detection model, we extracted the

all frame images from the video clips, and both

training and evaluations are performed for those

images.

3.2 Spoof Detection Accuracy

In this experiment, we compare the spoof detection

accuracy of the investigated methods with the

baseline methods and state of the art methods on two

databases: Replay-Attack Database and Spoofed

Fingerphoto Database. Table 4 and Table 5 show the

HTER (half total error rate) of the spoof detection

accuracies for the each attack type, such as printing,

photo, movies. For the Replay-Attack DB, we used

all frames in the movie clips both for the training and

testing.

Table 4 shows that the proposed DCNN method

outperforms the other methods. For the Fingerphoto

Spoof DB (Archit et al., 2016), spoof detection

accuracies of the proposed methods (NRPQA+SVM

and DCNN) exhibited less than 1/10 error rates

compared to the Archit et al. (2016) ’s LBP based

method.

Table 5 shows that the proposed DCNN method

outperforms the other methods, even better than the

other state-of-the-art DCNN method (Koichi et al.,

2017; Jianwei et al., 2014). The differences of the our

DCNN model and the Koichi’s DCNN model are the

preprocessing of input images and the cropping size

of the images. Our model compresses the input

images (256x256) before cropping, but Ito’s model

does not. The cropping size of our model is (227x227),

a little bit larger than Ito’s model (240x180). As is the

case with WLBP features, the image compression

process may contribute to capturing the differences

between the real /spoofed images. In the case of the

Replay-Attack Database, the method NRPQA+SVM

shows the low detection accuracies, especially for the

“Highdef” samples, which means high-resolution

photos and videos images. This may mean that the

examined NRPAQ features may be specific features

that is prominent to the Spoofed Fingerphoto

Database. But only our DCNN model shows high

accuracy (HTE < 2/10

-6

, by using “rule of three”) for

the “Highdef” images.

Table 4: Summary of the evaluation results (finger).

Half Total Error Rate（％）

Attack

Scenario

Proposed Methods

Baseline

Display

Capture

DCNN

WLBP+SVM

NRPQA+SVM

LBP+SVM

LPQ+SVM

LBP+SVM

[Tanja2016]

Nokia

0.024

0.05

0.0

0.66

4.71

6.05

OPO

0.024

0.02

0.0

0.42

1.83

4.85

iPad

Nokia

0.2

0.12

0.0

2.52

3.15

3.12

OPO

0.024

0.83

0.0

0.39

0.71

5.27

Nexus

Nokia

0.024

0.78

0.0

0.85

3.79

1.39

OPO

0.024

0.32

0.24

0.56

3.13

0.24

Laptop

Nokia

0.024

0.20

0.24

5.76

20.8

4.48

OPO

0.17

0.39

0.42

1.8

2.31

Total

0.04

0.9

0.06

3.56

4.8

3.71

Table 5: Summary of the evaluation results (face).

Half Total Error Rate（％）

Attack

Scenario

Proposed Methods

Baselines

State-of-

the-art

DCNN(Ours)

WLBP+SVM

NRPQA+SVM

LBP+SVM

LPQ+SVM

DCNN

[Itoh’17]

0.0

0.01

1.84

12.4

7.9

N/A

Mobile

0.0

0.4

1.14

2.5

25.0

N/A

Highdef

0.0

7.8

17.2

12.7

32.2

N/A

Total

0.01

4.98

11.1

16.5

30.2

0.52

To investigate that which parts of the image areas

contribute to detect spoofing, we adopted Grad-CAM

visualization method (Ramprasaath et al., 2016) to

our DCNN models.

Grad-CAM highlights importance of each

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

neurons for an each prediction. To obtain the class-

discriminative localization map (Fig. 5 (c)(d), Fig. 6

(c)(d)), Grad-CAM calculate the gradient of the score

for the class of interest (in this study, “real” or

“spoofed”), with respect to CNN feature maps (for

example, ‘relu5’ layer of the DCNN(AlexNet)).

Fig. 5 (a) is the original fingerphoto image, and

Fig. 5 (b) is the spoofed fingerphoto image, and (d) is

the Grad-CAM visualization of the image (b).

From Fig. 5 (a)(c), we can see that our DCNN

model detects spot areas of the genuine image, mainly

inside of the finger areas. On the other hand, for the

spoofed image, the model detects border areas

between background areas and finger areas.

(a) genuine (b) spoofed

Figure 5: (a-b) Original finger image and the generated

spoofed image. (c-d) Grad-CAM maps for the original

image and the spoofed image.

Fig. 6 (a) is the original face image, and (c) is the

Grad-CAM visualization of the image (a). Fig. 6 (b)

is the spoofed face image, and (d) is the Grad-CAM

visualization of the image (b).

From Fig. 6 (a)(c), we can see that our DCNN

model detects spot areas of the genuine image, mainly

lateral side of the face areas. On the other hand, for

the spoofed image, the model detects border areas

between background areas and face areas (but mainly

background areas), and covers more wider areas,

compared to the original (real) image.

Those visualization results suggest that our

DCNN models learns the border between background

areas and finger/face areas, and utilizes them to detect

spoof/genuine images.

(a) genuine (b) spoofed

Figure 6: (a-b) Original face image and the generated

spoofed image. (c-d) Grad-CAM maps for the original

image and the spoofed image.

3.3 Performance Comparison

We compare the performances of the investigated

spoofing detection methods (three types of

handcrafted features with SVM, and automatic

feature extraction/classification by using DCNN

(AlexNet)).

For the SVM methods based on handcrafted

features, we examined feature extraction + SVM

classification times by using linear SVM classifier

(liblinear). For the DCNN (AlexNet) based method,

we examined input image classification times.

Table 6 shows that DCNN and NRPQA are more

than 10 times faster than WLBP or LPQ. The

spoofing detection accuracies of those two methods

are also much better than the WLBP or LPQ.

Table 6: Summary of the performance (finger).

Processing time per 1000 images (second)

AlexNet

WLBP

NRPQA

LPQ

Feature

extraction

4.33

193.0

3.76

49.0

Classification

0.22

Total

4.33

193.0

3.98

49.22

3.4 Blurriness Tolerant Analysis

In the task of the spoof detection, presentation images

are affected by camera defocus or hand movements,

which are commonly seen in both printed photo,

replayed video attacks and real faces/fingers.

To simulate image distortions caused by camera

defocus or hand movements, we create blurred

images by applying image filters (Gaussian blur or

motion blur filters) to the test datasets. For the

Face/Fingerphoto Spoof Detection under Noisy Conditions by using Deep Convolutional Neural Network

training images, we do not apply image filters (only

use original data sets).

For the Gaussian blurriness, we changed the

standard deviation σ parameter, from 0.1 to 2.6 by

the step size 0.5. For the motion blurriness, we

changed the length of the PSF （ point spread

function）from 1 to 10 pixels by step size 1, and the

filter angle was fixed (11°).

(a)σ=0.1 (b) σ=2.6

(c)σ=0.1 (d) σ=2.6

Figure 7: Examples of Gaussian blurred images (face).

(a) PSF size=1 (b) PSF size=10

Figure 8: Examples of motion blurred images (face).

Fig. 13 and Fig. 15 show the spoof detection

accuracies for face and spoofed fingerphoto database

for Gaussian blurred images. Fig.14 and Fig. 16 show

the spoof detection accuracies for face and spoofed

fingerphoto database for motion blurred images. In

each figure, “NRPQA” represents the method

NRPQA defined in the section 2.1 (2), “LBPs”

represents the method WLBP defined in the section

2.1 (1), “LPQs” represents the method LPQ defined

in the section 2.1 (3) (but combined with the LPQ

from wavelet transformed images too), and “DCNN”

represents the method defined in the section 2.2.

“ALL” represents the features concatenation of all

“NRPQA”, “LBPs” and “LPQs”.

(a)σ=0.1 (b) σ=2.6

Figure 9: Examples of Gaussian blurred images (finger).

(a) PSF size=1 (b) PSF size=10

Figure 10: Examples of motion blurred images (finger).

In the case of Spoofed Fingerphoto Database, Fig.

13 shows that the more standard deviation of the

Gaussian blur increases, the accuracies of the spoof

detection decrease steeply. Especially it is prominent

for the NRPQA, which exhibited the hight

performance for the normal (no additive blurring)

images. NRPQA is based on the block noise of the

images, which may disappear by the blurring noises.

It is also the case for the motion blurring, as you can

see in the Fig. 14.

In the case of Replay-Attack Database, Fig. 15

and Fig. 16 also show that the more adding blurring

noise, the accuracies of the spoof detection decrease

steeply. In this case, the decreases of the accuracies

are almost all the same for the features “NRPQA”,

“LBPs”, and “LPQs”.

Among the examined methods, DCNN shows not

only the highest accuracy but also the highest

robustness for the blurring noises. To investigate the

stabilities of the examined DCNN model for the

increasing intensity of the blurring noise, we adopted

Grad-CAM visualization for blurred images. Fig. 11

shows the results of Grad-CAM visualizations for the

Gaussian blurred images. Fig. 12 shows the results of

Grad-CAM visualizations for the motion blurred

images. We can see that highlighted areas are not

affected by the increasing intensity of the Gaussian

blur, or motion blur. Those results support the results

of spoof detection accuracies of the DCNN models.

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

(a) σ=0.1 (b) σ=2.6

Figure 11: (a) Grad-CAM map for the Gaussian blurred

image ( σ =0.1). (b) Grad-CAM maps for the motion

blurred same image (σ=2.6).

(a) PSF size=1 (b) PSF size=10

Figure 12: (a) Grad-CAM map for the motion blurred

image (PSF size=1). (b) Grad-CAM maps for the motion

blurred same image (PSF size=10).

Figure 13: Spoof detection accuracy on the Gaussian blur

(finger).

Figure 14: Spoof detection accuracy on the motion blur

(finger).

Figure 15: Spoof detection accuracy on the Gaussian blur

(face).

Figure 16: Spoof detection accuracy on the motion blur

(face).

4 CONCLUSIONS

In this paper, we address the problem of face/finger

spoof detection for the generic camera based

biometrics, particularly under noisy conditions. We

first propose the anti-spoofing methods based on the

local texture features, and achieved less than 1/10 of

HTER (half total error rate) compared to the previous

methods, for the two different modality databases,

Replay-Attack Database (face) and Spoofed

Fingerphoto Database (finger).

Furthermore, to simulate the real-life scenarios,

we investigate spoof detection under additive noise,

such as defocused blurriness and motion blurriness.

Our experiments show that using the model trained

from clean data, most of the system performance

degrades significantly for blurred images. Among the

proposed methods, only the DCNN based method

shows not only the highest accuracy but also the

highest robustness for the blurred images.

For future work on spoof detection of the generic

camera based biometrics, we intend to include i)

evaluation under the ambient illumination in the use

case scenario of interest, ii) investigation for the

Face/Fingerphoto Spoof Detection under Noisy Conditions by using Deep Convolutional Neural Network

cross-modal scenario, iii) develop compact models

that can be used on mobile devices.

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