Iris Liveness Detection Methods in Mobile Applications

Ana F. Sequeira

1,2

, Juliano Murari

and Jaime S. Cardoso

1,2

INESC TEC (formerly INESC Porto), Porto, Portugal

Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

Universidade Federal de S. Paulo, S

ao Paulo, Brazil

Keywords:

Biometrics, Iris, Liveness Detection, Fake Database, Handheld Device.

Abstract:

Biometric systems are vulnerable to different kinds of attacks. Particularly, the systems based on iris are vul-

nerable to direct attacks consisting on the presentation of a fake iris to the sensor trying to access the system as

it was from a legitimate user. The analysis of some countermeasures against this type of attacking scheme is

the problem addressed in the present paper. Several state-of-the-art methods were implemented and included

in a feature selection framework so as to determine the best cardinality and the best subset that conducts to the

highest classiﬁcation rate. Three different classiﬁers were used: Discriminant analysis, K nearest neighbours

and Support Vector Machines. The implemented methods were tested in existing databases for iris liveness

purposes (Biosec and Clarkson) and in a new fake database which was constructed for evaluation of iris live-

ness detection methods in the mobile scenario. The results suggest that this new database is more challenging

than the others. Therefore, improvements are required in this line of research to achieve good performance in

real world mobile applications.

1 INTRODUCTION

Biometric systems can offer several advantages over

classical security methods as they rather identify an

individual by what he is instead of based on some-

thing he knows or possesses. However, in spite of

its advantages, biometric systems have some draw-

backs, including: i) the lack of secrecy (e.g. every-

body knows our face or could get our ﬁngerprints),

and ii) the fact that a biometric trait cannot be replaced

(no new iris can be generated if an impostor “steals”

it). Furthermore, biometric systems are vulnerable to

external attacks which could decrease their level of

security. Concerning these vulnerabilities we ﬁnd in

the literature (Galbally et al., 2007) an analysis of the

eight different points of attack on biometric recogni-

tion systems previously identiﬁed (Ratha et al., 2001).

These points are illustrated in Fig. 1.

Figure 1: Architecture of an automated biometric veriﬁca-

tion system. Possible attack points are numbered from 1 to

8, from (Galbally et al., 2007).

These attacks are divided into two main groups:

direct and indirect attacks.

• Direct Attacks: the ﬁrst vulnerability point in a

biometric security system is the possibility to gen-

erate synthetic biometric samples (for instance,

speech, ﬁngerprints or face images) in order to

fraudulently access a system. These attacks at

the sensor level are referred to as direct attacks.

It is worth noting that in this type of attacks no

speciﬁc knowledge about the system operation is

needed (matching algorithm used, feature extrac-

tion, feature vector format, etc). Furthermore, the

attack is carried out in the analogue domain, out-

side the digital limits of the system, so the digital

protection mechanisms (digital signature, water-

marking, etc.) can not be used.

• Indirect Attacks: this group includes all the re-

maining seven points of attack. Attacks 3 and 5

might be carried out using a Trojan Horse that

bypasses the feature extractor, and the matcher

respectively. In attack 6 the system database

is manipulated (a template is changed, added or

deleted) in order to gain access to the applica-

tion. The remaining points of attack (2, 4, 7 and 8)

are thought to exploit possible weak points in the

communication channels of the system, extract-

Sequeira A., Murari J. and Cardoso J..

Iris Liveness Detection Methods in Mobile Applications.

DOI: 10.5220/0004691800220033

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 22-33

ISBN: 978-989-758-009-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

ing, adding or changing information from them.

In opposition to direct attacks, in this case the in-

truder needs to have some information about the

inner working of the recognition system and, in

most cases, physical access to some of the ap-

plication components (feature extractor, matcher,

database, etc.) is required.

Among the different existing biometric traits, iris

has been traditionally regarded as one of the most re-

liable and accurate. This fact has led researchers to

pay special attention to its vulnerabilities and in par-

ticular to analyze to what extent their security level

may be compromised by spooﬁng attacks. These at-

tacks may consist on presenting a synthetically gen-

erated iris to the sensor so that it is recognized as the

legitimate user and access is granted. The most com-

mon and simple approaches are those carried out with

high quality iris printed images (Ruiz-Albacete et al.,

2008). However, other more sophisticated threats

have also been reported in the literature such as the

use of contact lenses (Wei et al., 2008).

The development of iris liveness detection tech-

niques is crucial for the deployment of iris biometric

applications in daily life. The evolution in the use

of mobile devices in our society also raises the urge

for liveness solutions in the mobile biometric ﬁeld.

To pursue this goal there is also a need for suitable

databases in which new methods can be tested.

In this work we implemented state-of-the-art

methods conceived to deal with spooﬁng attacks in

iris recognition, in particular, the use of printed im-

ages and contact lenses. The proposed method com-

prises a feature selection method, in order to deter-

mine the best cardinalities and respective subset of

features, with the use of state-of-the-art classiﬁers.

This framework intended to achieve the best classiﬁ-

cation rates with only the “necessary” number of fea-

tures Two existing databases were tested, one com-

prising samples of printed iris images and another

comprising images of eyes with contact lenses. Tak-

ing in account the results obtained and the character-

istics of the databases available and the new trend of

performing biometric recognition in mobile scenar-

ios, we constructed a new fake iris database. This

database comprises printed copies of the original im-

ages (after being printed, the images were acquired

with the same device and in similar conditions as the

original ones). We found this new database to be more

challenging than the others.

This paper is organized as follows. In section 2

the concept of liveness detection in an iris recogni-

tion system is presented. In section 3, we explain

the algorithms implemented. In section 4 is presented

the database constructed with fake printed images for

testing liveness detection methods in iris recognition.

In section 5, the dataset of images is presented in 5.1,

the methodology used is presented in 5.2 and the re-

sults and their discussion are presented in 5.3. Finally,

in section 6 we draw some conclusions and sketch

some ideas for future works.

2 IRIS LIVENESS DETECTION

The problem of liveness detection of a biometric trait

can be seen as a two class classiﬁcation problem

where an input trait sample has to be assigned to one

of two classes: real or fake. The key point of the pro-

cess is to ﬁnd a set of discriminant features which per-

mits to build an appropriate classiﬁer which gives the

probability of the sample vitality given the extracted

set of features (Galbally et al., 2012b).

Biometric recognition systems are vulnerable to

be spoofed by fake copies (Daugman, 2004), for in-

stance, fake ﬁnger tips made of commonly available

materials such as clay and gelatine. Iris is no excep-

tion. There are potential threats for iris-based sys-

tems, the main are (He et al., 2009):

• Eye image: Screen image, Photograph, Paper

print, Video signal.

• Artiﬁcial eye: Glass/plastic etc.

• Natural eye (user): Forced use.

• Capture/replay attacks: Eye image, IrisCode tem-

plate.

• Natural eye (impostor): Eye removed from body,

Printed contact lens.

The feasibility of some attacks have been reported

by some researchers (Daugman, 1998; Daugman,

2004; Lee et al., 2005) who showed that it is actually

possible to spoof some iris recognition systems with

printed iris and well-made colour iris lens. Therefore,

it is important to detect the fake iris as much as possi-

ble (He et al., 2009).

Several liveness detection methods have been pre-

sented through the past recent years. In fact, anti-

spooﬁng techniques were presented that use physio-

logical properties to distinguish between real and fake

biometric traits. This is done in order to improve the

robustness of the system against direct attacks and to

increase the security level offered to the ﬁnal user. Iris

liveness detection approaches can broadly be divided

into: i)software-based techniques, in which the fake

irises are detected once the sample has been acquired

with a standard sensor (i.e., features used to distin-

guish between real and fake eyes are extracted from

the iris image, and not from the eye itself), and ii)

IrisLivenessDetectionMethodsinMobileApplications

hardware-based techniques, in which some speciﬁc

device is added to the sensor in order to detect partic-

ular properties of a living iris such as the eye hippus

(which is the permanent oscillation that the eye pupil

presents even under uniform lighting conditions) or

the pupil response to a sudden lighting event (e.g.,

switching on a diode) (Galbally et al., 2012b). Ac-

cording to this author, even though hardware-based

approaches usually present a higher detection rate, the

software-based techniques have the advantage of be-

ing less expensive (as no extra device in needed), and

less intrusive for the user (very important character-

istic for a practical liveness detection solution). In

general, a combination of both type of anti-spooﬁng

schemes would be the most desirable approach to in-

crease the security level of biometric systems. (Gal-

bally et al., 2012b)

In this work we focus on software based tech-

niques since these are more easily and affordable ap-

plicable in real-world applications.

In the literature we found that the methods of live-

ness detection may be classiﬁed into four categories

based on the physical features of biometric and live-

ness data and the timing of measurement (Une and

Tamura, 2006). In this framework, the biometric data

are used in the iris recognition and the liveness data

are used in the liveness detection. We can itemize the

four categories:

• Perfect matching model: Both biometric and live-

ness data are simultaneously obtained from the

same physical feature.

• Simultaneous measuring model: Biometric and

liveness data are simultaneously obtained from

different physical features.

• Same biometric measuring model: Biometric and

liveness data are obtained from the same physical

feature with different timings.

• Independent measuring model: Biometric and

liveness data are obtained from different features

with different timings.

The ideal conﬁguration of liveness detection for bio-

metrics recognition is represented by the perfect

matching model with the highest ability to distinguish

between live and fake irises (Kanematsu et al., 2007).

The potential of quality assessment to identify real

and fake iris samples acquired from a high quality

printed image has previously been explored as a way

to detect spooﬁng attacks (Galbally et al., 2012b).

Some quality based features have been used individu-

ally for liveness detection in traits such as iris (Kane-

matsu et al., 2007; Wei et al., 2008) or face (Li et al.,

2004). A strategy based on the combination of sev-

eral quality related features has also been used for

spooﬁng detection in ﬁngerprint based recognition

systems (Galbally et al., 2012a) as well as in iris live-

ness detection (Galbally et al., 2012b). In this latter

work, a set of quality measures are used as iris live-

ness detection features to aid the classiﬁcation of fake

or real iris images included in a framework of feature

selection. We ﬁnd in literature that works concerning

the quality of iris images are often the starting point to

iris liveness detection techniques. One example is the

assessment of the iris image quality based on mea-

sures like occlusion, contrast, focus and angular de-

formation (Abhyankar and Schuckers, 2009), other is

the use of texture analysis of the iris (He et al., 2007),

among others like, for example, the analysis of fre-

quency distribution rates of some speciﬁc regions of

iris (Ma et al., 2003).

The way forward seems to be the development of

techniques for iris liveness detection that work well

independently of the particular characteristics of the

databases available nowadays. It is required to de-

velop and improve methods as well as to construct

new databases in less constrained conditions.

3 IMPLEMENTED METHODS

Some of the measures are obtained from the entire

eye image but others are extracted only from the iris

region, therefore a segmentation step is required. We

choose to make the segmentation process manually, in

order to ensure reasonable accuracy. The manual seg-

mentation is done by marking three differents points

in the image. The ﬁrst point is the eye centre, i.e., we

consider a single centre for both pupil and iris. The

second point is marked in the pupil border and the

third in the iris border. With these points is possible

to determine the iris and pupil radius and then approx-

imate the contours as two concentric circles. With

the manual segmentation’s information we are able to

map the regions of interest which will be eventually

used by the liveness detection algorithms.

3.1 Algorithm 1 - High Frequency

Power

The High Frequency Power algorithm, which pro-

vides feature 1, works on the whole image and mea-

sures the energy concentration in the high frequency

components of the spectrum using a high pass con-

volution kernel of 8x8. The application of this con-

volution is a good Fourier Transform approximation

and works as high frequency spectral analysis, which

can be considered an estimator of focus (Daugman,

2002). The focus of a real iris, as it is a 3D volume,

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

is different from a fake iris focus, which has a 2D sur-

face. For more details on the method see (Galbally

et al., 2012b).

3.2 Algorithm 2 - Local Contrast

The Local Contrast algorithm, which provides fea-

ture 2, is based on bounding box that involves the

iris and the pupil. The bounding box is divided in

blocks of P × P and for each block it is applied the

Fast Fourier Transform (FFT) algorithm to extract the

medium power frequencies, which better represents

the contrast. The ﬁnal value is given by the num-

ber of blocks with medium values (between 20 and

60) divided by the total number of blocks. This al-

gorithm was inspired in an occlusion estimation tech-

nique (Abhyankar and Schuckers, 2009) and it was

adapted for contrast estimation for iris liveness detec-

tion in (Galbally et al., 2012b) where more details can

be found.

3.3 Algorithm 3 - Global Contrast

The Global Contrast algorithm, which provides fea-

ture 3, explores the fact that parts extremely bright or

dark of the image are not useful and can be consid-

ered as noise. Thus, pixels near medium value (128

in 8-bit image) are considered of best contrast (Ab-

hyankar and Schuckers, 2009). In order to quantify

the contrast, the original pixels values are normalized

between 0 and 25 (Figure 2). Original pixels near

medium value will get higher values in the normalized

scale, as well as very low and very high values (< 10

and > 245) are normalized to 0. This measure was

presented in (Abhyankar and Schuckers, 2009) and

it was adapted for global contrast estimation for iris

liveness detection in (Galbally et al., 2012b) where

more details can be found.

Figure 2: Normalization function of the algorithm 3.

3.4 Algorithm 4 - Frequency

Distribution Rates

The Frequency Distribution Rates algorithm consists

in different mathematical combinations of three dif-

ferent parameters which consider respectively the

power of the low (F

), medium (F

), and high (F

)

frequencies (computed according to the 2D Fourier

Spectrum) from two iris subregions in the horizontal

direction. This subregions are illustrated in Figure 3.

Each subregion is subdivided in three circular concen-

tric region, which determine the three different fre-

quencies, i.e, for the ﬁrst subregion, F

refers to the

central circle, F

refers to the middle circular ring and

refers to the outer circular ring, as depicted in Fig-

ure 4. The ﬁnal F

is given by the average between the

two regions: F

. The same is done to F

and

. More details on the method can be found in (Ma

et al., 2003; Galbally et al., 2012b).

Figure 3: Example of the subregions used in the algorithm

4 (Galbally et al., 2012b).

Figure 4: One of the regions of interest subdivided to cal-

culate the frequencies (Galbally et al., 2012b).

With the three ﬁnal frequencies we extract seven

different combinations, represented in Table 1 (Ma

et al., 2003; Galbally et al., 2012b).

3.5 Algorithm 5 - Statistical Texture

Analysis

The Statistical Texture Analysis algorithm was devel-

oped as a contact lens countermeasure. The outer por-

tion of the colour contact lens (corresponding to re-

IrisLivenessDetectionMethodsinMobileApplications

Table 1: Extracted measures from the ﬁnal frequencies.

Features no. Combination

4 F

+ F

5 F

/(F

+ F

)

6 F

7 F

8 F

9 (F

+ F

)/F

10 (F

∗ F

)/F

gions closer to outer circle) provides the most useful

texture information for fake iris detection since this

section of the fake iris is insensitive to the pupil di-

lation (He et al., 2007). The region of interest is the

lower part of the iris in order to minimize the occlu-

sion by the eyelashes and eyelids, which in general

occurs in the upper iris portion. In order to achieve

invariance to translation and scale, the region of inter-

est is further normalized to a rectangular block of a

ﬁxed size W × H (Figure 5).

(a) Original (b) Normalized

Figure 5: Region of interest used in the algoritm 5 (He et al.,

2007).

After the normalization, the GLCM (Gray Level

Co-occurence Matrix), one of the most proeminent

approaches used to extract textural features (Haral-

ick et al., 1973), is calculated. Four measures are

extracted: the mean (µ) and standard deviation (σ),

direct from the normalized region of interest, and the

contrast (con) and the energy (e) from the GLCM ma-

trix. These measures will provide features 11 to 14

and its values are given, respectively, by the equations

below:

µ =

W ∗ H

∑

i=1

∑

j=1

I(i, j) (1)

σ =

W ∗ H

∑

i=1

∑

j=1

(I(i, j)−µ)

(2)

con =

∑

i=1

∑

j=1

(i − j)

P(i, j) (3)

e =

∑

i=1

∑

j=1

P(i, j)

(4)

Where I denotes the normalized iris image, W

is the width of the normalized iris image, H is the

height of the normalized iris image. P is the co-

occurrence matrix and N denotes the dimension of the

co-occurrence matrix. For more details on the method

see (He et al., 2007).

3.6 Feature Selection

The algorithms implemented originated 14 different

features. Due to this dimensionality it is possible that

the best classiﬁcation results are not obtained using all

the features, but a subset of them. It is convenient to

search for the optimum number and set of features.

To exhaustively test all possibilities is not feasible.

Therefore we use the “Sequential Forward Floating

Selection” (SFFS) (Pudil et al., 1994) to perform fea-

ture selection. The SFFS is basically a combination

of search methods such as “Plus-l-Minus-r” (Stearns,

1976) and Sequential Forward Search (SFS) (Whit-

ney, 1971). The appearance of “ﬂoating” comes from

the fact that the values l and r are not ﬁxed, i.e., they

can “ﬂoat”. Another aspect is the dominant direction

of search, including (forward) or excluding (back-

ward) characteristics (Pudil et al., 1994). We use

the Mahalanobis distance as criterion function. The

SFFS has shown to be competitive when compared to

other selection techniques (Jain and Zongker, 1997).

3.7 Classiﬁcation

The classiﬁcation results were obtained using three

classiﬁcation methods: Discriminant Analysis (DA),

k-Nearest Neighbour (kNN) and Support Vector Ma-

chine (SVM).

4 MobBIOfake: IRIS IMAGES

CAPTURED WITH A

HANDHELD DEVICE

The MobBIOfake database was constructed upon the

MobBIO Multimodal Database (Blind Ref, 2013).

The MobBIO Multimodal Database comprises the

biometric data from 105 volunteers. Each individ-

ual provided samples of face, iris and voice. The

equipment used for the samples acquisition was an

Asus Transformer Pad TF 300T, with Android ver-

sion 4.1.1. The device has two cameras, one frontal

and one back camera. The camera used was the back

camera, version TF300T-000128, with 8 MP of reso-

lution and autofocus.

The iris images were captured in two different

lighting conditions, in a room with both natural and

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

artiﬁcial sources of light, with variable eye orienta-

tions and occlusion levels, so as to comprise a larger

variability of unconstrained scenarios. Each volun-

teer contributed with 16 images (8 of each eye) with a

300 × 200 resolution. Some examples of iris images

are depicted in Figure 6.

(a) (b) (c) (d)

(e) (f) (g) (h)

Figure 6: Iris images from MobBIO database illustrating

different kinds of noise: a) Heavily occluded; b) Heavily

pigmented; c) Glasses reﬂection; d) Glasses occlusion; e)

Off-angle; f) Partial eye; g) Reﬂection occlusion and h)

Normal.

MobBIOfake

The MobBIOfake is composed by a subset of 800

iris images from MobBIO and its corresponding fake

copies, in a total of 1600 iris images. The fake sam-

ples were obtained from printed images of the original

ones captured with the same handheld device and in

similar conditions. From the original dataset of im-

ages

The aim of constructing such a database is, on one

hand, to fulﬁl the necessity of databases and, on the

other hand to broad the acquisition conditions of the

images. The number and variety of databases for iris

liveness detection is somewhat limited so the fact that

these images were captured with a portable device and

are RGB images come as a novelty and makes it possi-

ble to evaluate liveness methods in this new upcoming

scenario.

The construction of the MobBIOfake upon the

MobBIO iris images subset comprised several steps.

The images of each volunteer were joined in a single

image, as shown in Figure 7.

A preprocessing (contrast enhancement) was ap-

plied using GIMP software (GIMP, 2008) to the im-

age. This enhancement is believed to improve the

quality of the fake sample (Ruiz-Albacete et al.,

2008). After this, the images were printed in a profes-

sional printer using high quality photographic paper.

At this point we were able to capture the images. Each

individual image (a image of one single eye) was ac-

quired using the same portable device and in similar

lighting conditions as the original ones were captured,

as illustrated in Figure 8.

Figure 7: MobBIOfake construction: joint images of one

volunteer.

Figure 8: MobBIOfake construction: fake samples acquisi-

tion.

Finally, the individual eye images were cropped

and resized to ﬁx dimensions. An example of a real

image and its copy is depicted in Figure 9.

(a) Real image (b) Fake image

Figure 9: Corresponding real and fake images of MobBIO.

5 EXPERIMENTAL SETUP

5.1 Datasets

The implemented methods were tested in three

datasets. One was a database constructed within our

work, the MobBIOfake, comprised of 800 iris images

IrisLivenessDetectionMethodsinMobileApplications

and its corresponding fake copies, captured with the

same portable device and in similar conditions as the

original ones. The description of the construction of

this dataset was detailed in section 4.

The other two databases, described below, are the

Biosec database (Fierrez et al., 2007), composed by

real iris images and the corresponding fake printed

images; and the Clarkson database (S. Schuckers and

Yambay, 2013) comprising real iris images and fake

ones obtained by the use of contact lenses.

Biosec

The Biosec database was created at the Polytech-

nic University of Madrid (UPM) and the Universitat

Polit

ecnica de Catalunya (UPC). The images were ac-

quired in an ofﬁce room with a large table for hard-

ware of biometric recognition system and two chairs,

one for the donor and one for the supervisor of the ac-

quisition process. Environmental conditions such as

lighting and noise, were not controlled to simulate a

real situation (Fierrez et al., 2007). To construct the

false database original images are pre-processed and

printed on paper using a commercial printer. Then the

printed images were presented to the iris sensor, ob-

taining the fake copy. This study considered different

combinations of pre-processing, printing equipment

and paper type (Ruiz-Albacete et al., 2008). There-

fore, the database used consists of real and fake iris

images and follows the same structure as the original

database. Biosec dataset comprises a total of 1600

images: 800 real images and its corresponding 800

fake samples. All images are in greyscale and its di-

mensions are 640 × 480 (Galbally et al., 2012b). The

two eyes of the same individual are considered as dif-

ferent users. The acquisition of both real and fake

samples were made using the sensor LG IrisAccess

EOU3000 (Ruiz-Albacete et al., 2008).

Clarkson

The subset of Clarkson database that we used was

made available under request and contains 270 real

iris images and 400 fake iris images. The fake sam-

ples are images of eyes with contact lenses compris-

ing 14 models of contact lenses. There are two differ-

ent lighting conditions in the database, which was ac-

quired by video (capturing 100 frames and with vari-

ation of focus). The Clarkson database was made

available to participants of the LivDet-2013 compe-

tition (S. Schuckers and Yambay, 2013) .

5.2 Methodology

The proposed method is depicted in Figure 10.

Figure 10: Steps of the proposed method.

The ﬁrst step of the method is the segmentation, in

this case it was made manually so as to purge our re-

sults from the errors associated with automatic meth-

ods of iris segmentation. Although it has to be noted

that in a real world application this step needs to be

necessarily automatized.

The second step was the feature extraction. This

comprises the application of the methods described in

subsections 3.1, 3.2, 3.3, 3.4 and 3.5.

Next step was the feature selection. This com-

prises the application of the method Sequential For-

ward Floating Search, described in subsection 3, be-

fore applying the classiﬁers to evaluate the proposed

methods. We ran the SFFS to obtain the best subset

for each cardinality from ℵ = 2 to ℵ = 12 features.

This range is deﬁned by the selection method when

considering a set amount of 14 features.

The last step was the classiﬁcation. We used

three state-of-the-art classiﬁers: Discriminant Anal-

ysis (DA), k-Nearest Neighbours (kNN) and Sup-

port Vector Machines (SVM). For each cardinality,

(ℵ = 2,...,12), the results of classiﬁcation were ob-

tained calculating the average of the results of 50 runs

for classiﬁcation of the images based on the corre-

sponding best ℵ features. The results were obtained

by randomly dividing, in each run, the 1600 samples

in two sets: 1000 samples for training and 600 for

testing. The parameter k in kNN was optimized using

cross-validation, and tested in the interval [1,20] by

steps of 1. For the SVM, we used a polynomial ker-

nel and also used cross-validation for optimization of

the parameters. It was performing a ”grid-search” on

the parameters of the models. Exponentially growing

sequences of C were tested: C = 2

with N varying

between −1 and 15. For the polynomial degree, d,

values tested were: d = 1,2,3,4,5.

For the evaluation of the accuracy of the features

extracted in discriminating between fake and real im-

ages, we used the Equal Error Rate (EER). The EER

is obtained when the false acceptance rate (FAR)

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

and the False Rejection Rate (FRR) are equal. For

the classiﬁcation results we use the missclassiﬁcation

rate averaged over the 50 runs.

5.3 Experimental Results and

Discussion

In this section we present the results obtained by the

proposed method for iris liveness detection. The al-

gorithms applied return a set of 14 different features.

The ﬁrst step was to analyse individually each of

the 14 different features, for each image dataset. By

the analysis of the histogram obtained for fake and

real images we can from that moment have a hint

about which features will be good discriminative be-

tween fake and real images. For each histogram, the

threshold obtained considering equal error rate (EER)

allow us to determine the minimum error associated

with that feature, for the considered dataset. In Ta-

ble 2 are shown the minimum error values associated

with each feature for each database.

Table 2: Minimum error associated with each feature for

each database.

Associated Error (%)

Feature no. Biosec MobBIOfake Clarkson

1 (alg1) 31.3 31.8 35.4

2 (alg2) 17.2 31.2 26.3

3 (alg3) 21.1 21.9 26.7

4 (alg4) 15.1 40.8 31.5

5 (alg4) 43.6 27.9 36.0

6 (alg4) 14.4 42.9 31.5

7 (alg4) 15.6 33.0 31.3

8 (alg4) 15.2 30.8 31.9

9 (alg4) 39.9 26.4 36.3

10 (alg4) 15.8 29.3 31.8

11 (alg5) 22.9 27.2 32.2

12 (alg5) 17.2 35.7 37.8

13 (alg5) 13.2 35.7 39.1

14 (alg5) 25.8 29.3 37.9

It is clear from the Table 2 that each dataset has a

variable behaviour concerning the features obtained.

Simply observing the minimum errors (emphasized in

the table) we may conclude that the “best” feature for

one database is not necessarily the best for any of the

others.

To enlighten a bit more how the discriminative

power of each feature was analysed we show in Fig-

ure 11 the best and worse feature for each database.

This histograms illustrate clearly the efﬁciency of

each feature in discriminating real images from fake

images. For some features the lines for fake and real

images are well separated while for others this lines

are too much coincident compromising the separabil-

ity between the two classes.

The next step was to perform feature selection as

to avoid possible redundancies in the set of features.

Reducing the number of features to the strictly nec-

essary will improve the computational efﬁciency of

the method. In Table 3 are shown the best subset of

features for each cardinality, from 2 to 12, for each

database.

Again we observe the diversity of the results ob-

tained for each database. Another relevant aspect is

the combinations of features, in some cases we ob-

serve that features that individually do not have a

good performance when combined provide the best

subsets. This fact reinforces the pertinence of using a

method for feature selection.

Finally, Tables 4, 5 and 6 show the classiﬁcation

results for each cardinality, for each database, ob-

tained using the best subset determined by the feature

selection (averaged over the 50 runs).

Table 4: Classiﬁcation results for Biosec (classiﬁcation er-

rors in %).

ℵ

DA kNN SVM

µ σ µ σ µ σ

2 10.24 0.99 10.34 1.23 10.16 1.17

3 4.36 0.92 4.43 0.83 4.68 0.67

4 0.52 0.24 0.76 0.27 0.77 0.34

5 1.14 0.48 0.89 0.34 0.78 0.29

6 0.85 0.38 0.56 0.27 0.54 0.26

7 0.92 0.32 0.40 0.20 0.57 0.31

8 0.93 0.30 0.47 0.25 0.56 0.33

9 1.80 0.47 1.11 0.34 0.87 0.28

10 1.28 0.39 0.46 0.28 0.73 0.26

11 1.30 0.28 0.40 0.24 0.52 0.33

12 1.68 0.59 0.37 0.24 0.50 0.26

Table 5: Classiﬁcation results for MobBIOfake (classiﬁca-

tion errors in %).

ℵ

DA kNN SVM

µ σ µ σ µ σ

2 18.03 1.31 16.52 1.47 17.29 1.13

3 29.29 1.54 17.34 1.25 20.69 1.83

4 17.50 1.42 12.62 1.18 14.36 0.94

5 18.03 1.30 13.00 1.26 14.18 1.20

6 18.29 1.44 12.82 1.02 14.33 1.15

7 18.88 1.35 13.27 1.35 14.55 1.27

8 18.31 1.11 13.52 1.21 13.74 1.28

9 18.34 1.28 14.44 1.25 14.11 2.22

10 17.58 1.38 13.92 1.19 13.39 1.01

11 17.15 1.35 14.51 1.44 12.53 1.39

12 17.25 1.12 14.45 1.21 12.50 1.21

IrisLivenessDetectionMethodsinMobileApplications

(a) Biosec - Feature 13 (best result). (b) Biosec - Feature 5 (worse result).

(e) Clarkson - Feature 2 (best result). (f) Clarkson - Feature 13 (worse result).

Figure 11: Histograms for the best result/smallest minimum error (left) and worse result/biggest minimum error (right) for

each database.

The overall best results were obtained for Biosec

database and the worst overall results were obtained

for MobBIOfake. This is not a surprising result

since we expected this latter to be a more challeng-

ing database due to its characteristics. It was noto-

rious from the study of features individually that this

database presented the worse results. We interpret this

fact as a sign that new databases were needed for the

research of liveness in new scenarios.

Comparing the classiﬁers, we conclude that DA

led to worse results. This fact is also not surprising

since this classiﬁer may be considered simpler than

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

Table 3: Best subset of features for each cardinality, for each database.

ℵ

Subset of features

Biosec MobBIOfake Clarkson

2 [1 6] [3 10] [3 14]

3 [1 2 11] [5 8 10] [9 11 14]

4 [1 2 6 11] [3 5 8 10] [3 9 11 14]

5 [1 2 6 11 13] [3 4 7 8 10] [2 3 9 11 14]

6 [1 2 6 11 12 13] [3 4 7 8 9 10] [1 2 3 9 11 14]

7 [1 2 5 6 11 12 13] [3 4 5 7 8 9 10] [1 2 3 9 11 12 14]

8 [1 2 5 6 7 11 12 13] [3 4 5 7 8 9 10 13] [1 2 3 5 9 11 12 14]

9 [1 2 5 6 7 9 10 11 13] [3 4 5 7 8 9 10 12 13] [1 2 3 5 9 11 12 13 14]

10 [1 2 5 6 7 9 10 11 12 13] [3 4 5 7 8 9 10 11 12 13] [1 2 3 4 5 6 9 11 12 14]

11 [1 2 3 5 6 7 9 10 11 12 13] [2 3 4 5 7 8 9 10 11 12 13] [1 2 3 4 5 6 9 11 12 13 14]

12 [1 2 3 5 6 7 9 10 11 12 13 14] [1 2 3 4 5 7 8 9 10 11 12 13] [1 2 3 4 5 6 7 9 11 12 13 14]

Table 6: Classiﬁcation results for Clarkson (classiﬁcation

errors in %).

ℵ

DA kNN SVM

µ σ µ σ µ σ

2 29.25 2.48 18.86 2.38 21.63 2.65

3 23.38 2.25 18.38 2.06 16.29 2.55

4 20.15 2.61 10.64 1.85 9.20 2.16

5 17.53 2.47 7.82 1.90 7.03 1.62

6 15.74 2.08 8.89 1.71 7.45 2.10

7 14.36 2.01 8.32 2.16 6.77 1.41

8 14.55 1.88 9.50 1.63 7.57 1.75

9 12.99 2.49 8.88 1.74 6.92 2.22

10 11.03 1.92 7.86 1.65 5.89 1.86

11 11.02 2.11 7.17 1.32 5.74 1.56

12 14.33 3.17 7.51 1.82 5.69 1.65

the others. The kNN achieved the overall best results.

Now, analysing each database per se, we observe

for the Biosec database that the best average classi-

ﬁcation rate was obtained with kNN. In terms of the

cardinality of features, we note that the best average

result, 0.37%, obtained with a subset of 12 features, is

followed closely by the value 0.4% with only a cardi-

nality of 7. And this again encourages the use of fea-

ture selection since the computational time and com-

plexity is lowered if we lower the number of features.

Concerning the MobBIOfake, undoubtedly the

classiﬁcation errors obtained are higher than the other

databases, what is not unexpected as we already re-

ferred. The best average results were obtained with

the SVM classiﬁer, 12.50% , but corresponding to a

high cardinality, 12. Not very far form this value we

ﬁnd a subset with much lower cardinality, 4, for the

kNN, with an average error of 12.62%.

Analysing the Clarkson results, we note that the

combination of features improved considerably the

results when compared with the performance of the

features individually. The best average result was ob-

tained with SVM, 5.69%, this value is not as good

as the Biosec best result but is better than the Mob-

BIOfake one, but unfortunately it reffers to a subset

of high cardinality, 12. However, we may ﬁnd a 3

best value with a cardinality of 7.

6 CONCLUSIONS AND FUTURE

WORK

The actuality of the iris liveness detection topic is un-

questionable. As the ﬁeld of application of iris recog-

nition broads, to embrace the demands of a society

highly dependant on mobile and portable devices, the

necessity of improving the security urges. To achieve

new methods it is also necessary to explore new sce-

narios of image acquisition and this leads to the neces-

sity of adequate, freely, public available databases.

In this work, we constructed a new database for

iris liveness detection purposes with images acquired

in unconstrained conditions and with a handheld de-

vice. This database was tested for state-of-the-art

methods and the results were compared with the re-

sults obtained for two existing and tested databases.

The MobBIOfake database proved itself to be more

challenging and our results may not be considered sat-

isfactory but lead to a new more challenging scenario.

Published works present methods tested with ex-

isting databases which achieve excellent results, (0%

error classiﬁcation rate). However, we note that some

of these methods are closely connected with the par-

ticular database characteristics. The results with this

database did not achieve that excellent accuracy, but

we consider this justiﬁable by the fact that we avoided

the use of methods strongly dependent on the images

used, such as ratios of iris and pupil radius or areas,

among others.

For future work, we foresee the necessity of im-

proving the existing methods and develop new ones

IrisLivenessDetectionMethodsinMobileApplications

more suitable to the new imaging scenarios. Another

aspect to invest is the segmentation step which prefer-

ably should be automatic, however, the iris segmenta-

tion problem constitutes by itself a whole new set of

challenges.

We participated in an iris liveness competition, the

“LivDet Competition 2013” (Clarkson University and

of Technology, 2013a), held as part of the IEEE BTAS

2013

. We applied this methodology combined with

an automatic segmentation method (Monteiro et al.,

2013; Monteiro et al., 2014) and achieved the ﬁrst

place

ACKNOWLEDGEMENTS

The ﬁrst author would like to thank the Fundac¸

para a Ci

encia e Tecnologia (FCT) - Portugal for the

ﬁnancial support for the PhD grant with reference

SFRH/BD/74263/2010. The second author would

like to thank the National Council for Scientiﬁc and

Technological Development (CNPq) - Brazil.

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