An Efficient Contact Lens Spoofing Classification
Guilherme Silva
1, a
, Pedro Silva
1, b
, Mariana Mota
2 c
, Eduardo Luz
1 d
and Gladston Moreira
1 e
Computing Department, Federal University of Ouro Preto (UFOP), MG, Brazil
Department of Control and Automation Engineering, UFOP, MG, Brazil
Spoofing, CNN, EfficientNets.
Spoofing detection, when differentiating illegitimate users from genuine ones, is a major problem for biometric
systems and these techniques could be an enhancement in the industry. Nowadays iris recognition systems
are very popular, once it is more precise for person authentication when compared to fingerprints and other
biometric modalities. Nevertheless, iris recognition systems are vulnerable to spoofing via textured cosmetic
contact lenses and techniques to avoid those attacks are imperative for a well system behavior and could be
embedded. In this work, attention is centered on a three-class iris spoofing detection problem: textured/colored
contact lenses, soft contact lenses, and no lenses. Our approach adapts the Inverted Bottleneck Convolution
blocks from the EfficientNets to build deep image representation. Experiments are conducted in comparison
with the literature on two public iris image databases for contact lens detection: Notre Dame and IIIT-Delhi.
With transfer learning, we surpass previous approaches in most of the cases for both databases with very
promising results.
Nowadays, the term biometry (bio, life, plus metry,
measure, the measure of life) has been associated with
the measurement of physical, psychological or be-
havioral features of a living being. Biometric-based
person identification systems have been developed
rapidly in the last two decades. It is commonly ap-
plied not only to distinguish but also to identify some-
one based on their uniqueness, physical and biologi-
cal characteristics (Prabhakar et al., 2003).
What makes biometric measurements reliable is
the premise that each one is unique and has differ-
ent physical and behavioral characteristics (the voice,
way of walking, etc.). Today, various parts of the hu-
man body can be used, such as fingerprint, face, iris
and palm.
Among all the body parts, the iris is considered the
most promising, reliable, and accurate biometric trait,
These authors contributed equally to this work.
providing a rich texture that allows high discrimina-
tion among subjects and its uniqueness factor (Flom
and Safir, 1987; Daugman, 1993).
Daugman (1993), proposed the first functional
iris recognition method, whereas, in (Flom and Safir,
1987) the first patent using iris texture as biometric
modality are presented. Since then, several iris recog-
nition approaches have been proposed in the litera-
ture (Bowyer et al., 2008; Song et al., 2014) and the
modality has become popular in commercial biomet-
ric systems. Hence, iris modality becomes a target
for attacks (Sequeira et al., 2014b; Yadav et al., 2014;
Bowyer and Doyle, 2014) due to its use in forensics
systems (Yang et al., 2020).
There are several manners to attack an iris biomet-
ric system (Agarwal and Jalal, 2021; Morales et al.,
2021), such as using printed iris images (Sequeira
et al., 2014b), or by contact lenses (Yadav et al.,
2014; Bowyer and Doyle, 2014), for instance. These
sort of attacks are usually referred to in the literature
as a spoofing attack (Ming et al., 2020), specific as
iris spoofing. Upon this fact, Daugman (2003) pre-
sented a method for contact lens patterns detection.
Some works were proposed to dealing with this prob-
lem (Bowyer and Doyle, 2014; Menotti et al., 2015;
Raghavendra and Busch, 2015). However, some au-
Silva, G., Silva, P., Mota, M., Luz, E. and Moreira, G.
An Efficient Contact Lens Spoofing Classification.
DOI: 10.5220/0010868500003179
In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 441-448
ISBN: 978-989-758-569-2; ISSN: 2184-4992
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
thors use different definitions of iris spoofing detec-
tion, where liveness and counterfeit detection terms
are used with different meanings and, in some cases,
interchangeably (Sun et al., 2014). Works as in (Se-
queira et al., 2014a; Menotti et al., 2015; Galbally
et al., 2014) classify an iris image as real/live or as
fake, where a fake image is not a live one (e.g., printed
image). In addition, some works consider counterfeit
iris with printed color contact lenses as fake images
and iris images with soft/clear or no lenses as real im-
ages (Wei et al., 2008; Baker et al., 2010; Zhang et al.,
2010; Kohli et al., 2013; Doyle et al., 2013; Komu-
lainen et al., 2014).
Several works in the literature emerged to solve
the contact lens detection issue (Zin et al., 2021),
and several of them have reported accuracy over
98% (Wei et al., 2008; He et al., 2009; Zhang et al.,
2010). However, since contact lens technology is un-
der constant development, robust detection has be-
come a difficult task (Bowyer and Doyle, 2014). In
addition to this, studies found in the literature are fa-
vored by their methodology due to the use of datasets
containing contact lenses from a single manufacturer
among both training and test data (Bowyer and Doyle,
2014; Wei et al., 2008). According to (Doyle et al.,
2013), in a more realistic scenario, methods whose
accuracy is close to 100% could decrease to below
60% when a cross-sensor evaluation is performed.
In that sense, the datasets presented in (Yadav
et al., 2014) introduces a more complex three-class
image detection problem, in which iris images may
appear with textured (colored) contact lenses, soft
contact lenses (prescripted) and without lenses (no).
Addressing that, Raghavendra et al. (2017) proposed
an approach using a Deep Convolution Neural Net-
work (D-CNN) for contact lens detection. A new
architecture called ContlensNet is elaborated, which
consists of fifteen layers. The proposed approach is
tested in three scenarios: Intra-sensor (training and
testing with data from the same sensor), Inter-sensor
(training and testing with data from different sen-
sors), and Multi-sensor (training and testing with data
from multiple iris sensors combined). The authors
reported a Correct Classification Rates (CCR) which
overcomes the current state-of-the-art for the two well
known datasets for iris spoofing (both used in this
Choudhary et al. (2019) introduced a Densely
Connected Contact Lens Detection Network
(DCLNet) to detect contact lenses in iris images
captured from heterogeneous sensors. The authors
customized DenseNet121 and fine-tuned it with Near
Infra-Red (NIR) iris images. The experimental CCR
results reported for the IIITD Cogent dataset are
of 99.10% and 92.10% for the intra-sensor and the
multi-sensor scenario, respectively.
This work addresses a three-class problem, as well
as the one presented in (Silva et al., 2015), explor-
ing the Inverted Bottleneck Convolution (MBconv)
blocks (Tan and Le, 2019) which were used to report
state-of-the-art results for the ImageNet dataset (Tan
and Le, 2019). Our approach is compared against
three works on the literature (Yadav et al., 2014; Silva
et al., 2015; Raghavendra et al., 2017) in three evalu-
ation scenarios using two public datasets: 2013 Notre
Dame Contact Lens Detection (NDCL) dataset and
IIIT-Delhi Contact Lens Iris (IIIT-D). The proposed
approach outperformed the current state-of-the-art ap-
proach in five out of ten scenarios and presents com-
parable results in the five remaining.
The paper is organized as follows. First of all, we
present and describe in Section 2 the datasets used in
our experiments. The methodology proposed to han-
dle the spoofing detection is detailed in Section 3. Ex-
periments and results are described and discussed in
Section 4. Finally, conclusions and directions for fu-
ture work are outlined in Section 5.
In (Yadav et al., 2014), a three-class dataset is created
to evaluate an approach for contact lens iris detec-
tion. In this section, the characteristics of the datasets
used in our experiments are summarized in Table 1
and the following subsections. Both datasets, NDCL
and IIIT-D, are available upon public request and all
images are gray-scale with 640 × 480 pixels.
2.1 Notre Dame Contact Lens Dataset
The 2013 Notre Dame Contact Lens Detection (ND-
CLD’13 or simply NDCL) is a dataset where all im-
ages were acquired under near-IR illumination using
two sensors, LG4000 and IrisGuard AD100. The
dataset is divided into two subsets according to this
two sensors: LG4000 with 3000 images for training
and 1200 for verification; AD100 with 600 for train-
ing and 300, verification. This dataset contains a total
of 5100 images (Doyle and Kevin, 2014), all of them
are 640×480 pixels. Some samples of the NDCL and
its cameras and classes can be found in Fig. 1.
The images are equally divided among three
classes: (1) wearing cosmetic contact lenses, (2)
wearing clear soft contact lenses, and (3) wearing no
contact lenses. Each image is annotated with some
information: an ID to the subject it belongs, eye (left
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
Table 1: Main features of the datasets considered herein and introduced in (Yadav et al., 2014).
Dataset Sensor
# Training # Testing/Verification
Text. Soft No Total Text. Soft No Total
IrisGuard AD100 200 200 200 600 100 100 100 300
LG4000 iris camera 1000 1000 1000 3000 400 400 400 1200
Multi-camera 1200 1200 1200 3600 500 500 500 1500
Cogent Scanner 589 569 563 1721 613 574 600 1787
Vista Scanner 535 500 500 1535 530 510 500 1540
Multi-scanner 1124 1069 1063 3256 1143 1084 1100 3327
Figure 1: Samples of images in the 2013 Notre Dame Con-
tact Lens Detection (NDCL) dataset and IIIT-Delhi Con-
tact Lens Iris (IIIT-D). In the first row present samples from
NDCL IrisGuard AD100, the second, NDCL LG4000 iris
camera, while in the third, IIIT-D Cogent Scanner and the
last line, IIIT-D Vista Scanner. The first column presents
samples with textured/cosmetic contact lenses. The second
column presents samples with soft/clear/prescript contact
lenses. The third column presents samples with no contact
and right), the subject’s gender, race, the type of con-
tact lenses used, and the coordinates of pupil and
iris. More specific details of this dataset can be found
in (Doyle and Kevin, 2014, Section II.B).
2.2 IIIT-D Contact Lens Iris Dataset
The Indraprastha Institute of Information Technology
(IIIT)-Delhi Contact Lens Iris (IIIT-D) is a dataset
where the iris location information is not provided.
Nevertheless, for this work, we manually annotate all
images to assess segmentation impact. There is a total
of 6583 iris images from 101 different subjects. For
each individual: (1) 202 iris classes (different iris) be-
cause both eyes, left and right, were captured; (2) it
was used different textured lenses (colors and manu-
facturers); (3) the iris images were captured with soft
and textured lens and without - the three classes con-
sidered here. Images of this dataset are illustrated in
Fig. 1. More specific details of this dataset can be
found in (Doyle and Kevin, 2014, Section II.A).
This work methodology for iris contact lens detection
is based on deep representations with the state-of-the-
art inverted bottleneck convolutional blocks (Sandler
et al., 2018) and transfer learning (Goodfellow et al.,
We adapt the EfficientNets architecture (Tan and
Le, 2019) for the current problem, preserving some
pre-trained layers (from ImageNet). This section
presents a brief explanation of EfficientNets as well
as the new architecture proposed. The evaluation pro-
tocol is also detailed.
3.1 EfficientNet and Proposed
EfficientNet is a set of architectures based on
Mobile Inverted Bottleneck Convolution (MBconv)
blocks (Sandler et al., 2018). They were first pre-
sented in (Tan and Le, 2019), where the authors
proposed a uniform scaling of the network’s width,
depth, and resolution using a compound coefficient
(φ) that determines the dimensions of the model as
depth = α
width = β
resolution = γ
s.tα · β
· γ
α 1, β 1, γ 1
An Efficient Contact Lens Spoofing Classification
in which α, β and γ are constants obtained with a grid
search performed in (Tan and Le, 2019). Figure 2 ex-
emplifies the proposed scaling effect on the network
The EfficientNet is used in this work due to a su-
perior accuracy performance compared to previous
CNN on ImageNet (Russakovsky et al., 2015) while
being 6.1x faster and 8.4x smaller (Tan and Le, 2019).
It is worth mentioning that in (Tan and Le, 2019), the
authors proposed a CNN family with eight architec-
tures (B0 – B7) obtained with different φ values.
In this work, we have empirically chosen to use
the EfficientNet - B3 model, as it demonstrated the
best cost-benefit ratio between CRR and network size.
More details of the architecture are found in (Tan and
Le, 2019).
The EfficientNet models were originally proposed
to be used on ImageNet (Russakovsky et al., 2015).
However, we propose its use in a new classification
problem based on iris and contact lens. To better suit
our specific problem, we included extra blocks at the
end of original architecture. A fully connected lay-
ers (FC) is included to adjust the final classification
to the new classes and other layers with activation,
optimization, and regularization. Among these, we
have Dropout, a technique widely used in neural net-
works to avoid overfitting by dropping random units
during the training (Srivastava et al., 2014) and batch
normalization (BN), used to normalize the values in
intermediate layers to zero-mean and constant stan-
dard deviation (Bjorck et al., 2018). The activation
used in these blocks was the Switch activation func-
tion (Ramachandran et al., 2017). Table 2 summarizes
the model obtained named as EfficientNet B3 Lens
Detection (EB3LD).
Table 2: Proposed architecture, considering B3 EfficientNet
model as the base model. (NC = Number of Classes).
Stage Operator Resolution # channels # layers
1-9 EfficientNet 300 x 300 3 1
10 BN/Dropout 10 x 10 1536 1
11 FC/BN/Swich/Dropout 1 512 1
12 FC/BN/Swich 1 512 1
13 FC/Softmax 1 NC 1
The new layers are added after the Average Pool
Layer (“avg pool”) of the original EfficientNet - B3.
3.2 Pre-processing and Transfer
The Convolutional Neural Networks are capable of
extracting relevant features from input images, so they
do not require heavy pre-processing in many cases.
In this way, the only processing performed in our
database was the clipping of the iris in the image,
adding a 10% margin around it. The NDCL base al-
ready comes with the annotation of the iris coordi-
nates. However, the IIIT-D database required manual
marking of the same for later clipping.
To improve the model’s performance without the
need to generate new samples, we chose to use trans-
fer learning. In transfer learning, the weights learned
in a different problem are used for fine-tuning in an-
other one (Pan and Yang, 2010). This technique is
based on the idea that the initial layers can extract in-
termediate features from the input images, thus, the
weights learned from a wide database can be reused
as a starting point in training a new network. It is use-
ful when the training data is scarce and also to speed
up training convergence (Pan and Yang, 2010). Once
the pre-trained weights were loaded, the network was
trained with the new classification layers and blocks
specified in Table 2.
3.3 Evaluation
We evaluate the performance of our system in
three scenarios: intra-sensor, inter-sensor, and multi-
sensor. In the first case, we trained the proposed
model with samples from a single sensor and tested
with different samples from the same sensor and
database. Thus, we trained and tested the model with
sensor LG400 from the NDCL database and repeated
the process for the IrisGuard AD100 sensor. We also
trained and tested the model with the IIIT-D Cogent
Scanner and Vista Scanner, separately.
The second scenario proposed here is the inter-
sensor scenario. In this case, we used one sensor for
training and tested with a different one, both from the
same database. So, we trained with LG400 and tested
with AD100, and vice versa, and performed the ana-
log experiment with IIIT-D sensors. The objective
here is to evaluate a scenario closer to real applica-
tions, where the system is not always used with the
same sensor used to capture the training samples.
Finally, we investigated the impact of stacking
samples captured with different sensors for training,
characterizing the multi-sensor scenario. For this, we
trained the network with images obtained with both
NDCL sensors and tested with both as well, but never
using the same image for training and testing. Like-
wise, we trained the network with the two IIIT-D sen-
sors and evaluated the model with samples from both.
The metric used to evaluate our approach was the
correct classification rate (CCR) (Silva et al., 2015).
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
Figure 2: Compound scaling method and the effect of fixed ratio on the three dimensions. (Adapted from (Tan and Le, 2019)).
Table 3: CCR(%) results for Intra-sensor on the NDCL and IIIT-D databases.
MLBP (Yadav et al., 2014) 77.67 80.04 73.01 80.04
CLDnet (Silva et al., 2015) 78.33 86.00 69.05 72.08
ContlensNet (Raghavendra et al., 2017) 95.00 96.91 86.73 87.33
EB3LD 89.67 94.33 93.11 96.89
Table 4: CCR(%) results for Inter-sensor on the NDCL and IIIT-D databases. The first sensor is the train one, and the second,
LG4000 AD100 Vista Cogent
MLBP (Yadav et al., 2014) 60.08 61.03 77.79 65.29
CLDnet (Silva et al., 2015) 78.00 75.33 45.54 43.08
ContlensNet (Raghavendra et al., 2017) 90.45 88.00 84.80 94.80
EB3LD 78.83 91.00 82.85 70.71
The EB3LD approach implementation is conducted
using the Keras/TensorFlow framework. This model
requires all images resized to 300 × 300 pixels and
3 channels (RGB). Since our datasets are in gray-
scale, the three channels are replicated using the gray-
scale image. The CNN models are trained on a
GPU GeForce Titan X with 12GB with a Intel(R)
Core(TM) i9-10900 CPU @ 2.80GHz and a RAM of
The EB3LD model is trained for 20 epochs using
the Adam optimizer with a mini-batch size of 20 and
the categorical cross-entropy loss. The training data
is shuffled and 10% is used as validation data. The
initial learning rate is set to 10
with the callback of
reducing learning rate on the plateau by a factor of 0.5
and patience equal to 2 if the accuracy on validation
data stopped improving. Furthermore, the ImageNet
weights are used for transfer learning.
The results obtained are compared against the
literature (Yadav et al., 2014; Silva et al., 2015;
Raghavendra et al., 2017). Tables 3, 4 and 5 present
CCRs for contact lens classes detection problem on
three scenarios: intra, inter, and multi-sensor evalua-
tions, respectively. These results are analyzed as fol-
4.1 Intra-sensor Evaluation
According to Table 3, the proposed EB3LD approach
outperforms the literature in the IIIT-D sensors, in
which the difference among the other methods was
The worst performance is observed in AD100 sen-
sor from the NDCL dataset which has a small num-
ber of training data, only 600. It is also observed
when compared to the results reported by Raghaven-
An Efficient Contact Lens Spoofing Classification
Table 5: CCR(%) results for Multi-sensor on the NDCL and IIIT-D databases.
MLBP (Yadav et al., 2014) 73.20 72.96
CLDnet (Silva et al., 2015) 82.80 69.28
ContlensNet (Raghavendra et al., 2017) 94.65 92.60
EB3LD 94.73 94.73
dra et al. (2017). A comparable performance is ob-
served on LG4000 (NDCL) sensor. However, the ap-
proach proposed in (Raghavendra et al., 2017) has
more pre-processing (segmentation and normaliza-
tion) and each image has to be processed 32 times by
the network. In contrast, the proposed approach is a
single feed-forward in the network.
4.2 Inter-sensor Evaluation
According to Table 4, the proposed approach outper-
formed the literature results for the NDCL database
when training with images from the LG4000 sensor.
Overall, the transfer learning approach has shown less
efficiency for the inter-sensor scenario. Although the
EB3LD method did not overcome the literature in the
IIT-D sensors scenario, results show that iris location
improves EB3LD and ContlensNet classification. Our
hypothesis to lower performance on the inter-sensor
evaluation is overfitting. Both network architectures
(ContlensNet and EB3LD) generate high dimension
representation vectors, which could cause overfitting
due to overtraining. We hypothesize that the models
captured specific details of the sensors.
4.3 Multi-sensor Evaluation
According to Table 5, The EB3LD method outper-
forms the literature results in the multi-sensor sce-
nario for both NDCL and IIIT-D database. In this
sense, for this case, the transfer learning has obtained
impressive results. Our hypothesis for low perfor-
mance for the inter-sensor case and high performance
for the multi-sensor is specialization in the sensors
signature. Models could be learning specific details
regarding sensor technology and causing overfitting
when only one sensor is used for the training. The
Multi-sensor scenario is the closest to the real test,
once the models would be trained with data from sev-
eral sensors, which would decrease specialization on
a specific sensor. Thus, multi-sensor results shown
the suitability of our method for contact lens prob-
4.4 Discussion
The proposed approach had converged for all datasets,
even for the smallest one (NDCL-AD100). However,
it is latent that the EB3LD takes advantage of more
data as seen in all training using the sensors NDCL-
LG4000, and IIIT-D Cogent and Vista.
In the inter-sensor scenario, the model did not
generalize well for the IIIT-D sensors. Two hypothe-
ses arise: (i) over-training on the sensors’ data, or (ii)
the learned characteristics from a sensor are not useful
for the other. The second hypothesis is supported by
the results presented in Table 5 in which the EB3LD
approach presented state-of-the-art results.
In this paper, we proposed deep image representations
through the Inverted Bottleneck Convolution (MB-
conv) blocks by adapting the EfficientNet B3 network
for contact lens detection problem. The proposed
model could be embedded and work as one step of
the iris recognition pipeline.
The proposed model is called in this work
EB3LD. Experiments results revealed that the pro-
posed EB3LD model approach surpasses the litera-
ture in ve out of 10 scenarios, including NDCL and
IIIT-D databases. The proposed method allows the us-
age of a deeper network with a reduced number of im-
ages. The main limitation of the proposed approach
is related to the small number of training images such
as the one observed in the NDCL-AD100 dataset.
One future research direction would be to investi-
gate the transference of learning from another domain
or task, such as face or eye. Another direction is to
use a different representation of the image and apply
Data Augmentation techniques and evaluate the pro-
posed approach in an embedded scenario to be used
in industry.
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
The authors would also like to thank the Coordenac¸
de Aperfeic¸oamento de Pessoal de N
ıvel Superior
- Brazil (CAPES) - Finance Code 001, Fundac
de Amparo
a Pesquisa do Estado de Minas Gerais
(FAPEMIG, grants APQ-01518-21), Conselho Na-
cional de Desenvolvimento Cient
ıfico e Tecnol
(CNPq) and Universidade Federal de Ouro Preto
(UFOP/PROPPI) for supporting the development of
the present study. We gratefully acknowledge the sup-
port of NVIDIA Corporation with the donation of the
Titan X Pascal GPU used for this research.
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