Self-Supervised-Based Multimodal Fusion for Active Biometric
Verification on Mobile Devices
Youcef Ouadjer
1
, Chiara Galdi
2
, Sid-Ahmed Berrani
1,3
, Mourad Adnane
1
and Jean-Luc Dugelay
2
1
´
Ecole Nationale Polytechnique, 10 Rue des Fr
`
eres Oudek, 16200 El Harrach, Algiers, Algeria
2
Department of Digital Security, EURECOM, 450 Route des Chappes, 06410 Biot, France
3
National School of Artificial Intelligence, Route de Mahelma, 16201 Sidi Abdellah, Algiers, Algeria
Keywords:
Active Biometric Verification, Multimodal Fusion, Self-Supervised Learning.
Abstract:
This paper focuses on the fusion of multimodal data for an effective active biometric verification on mobile
devices. Our proposed Multimodal Fusion (MMFusion) framework combines hand movement data and touch
screen interactions. Unlike conventional approaches that rely on annotated unimodal data for deep neural
network training, our method makes use of contrastive self-supervised learning in order to extract powerful
feature representations and to deal with the lack of labeled training data. The fusion is performed at the
feature level, by combining information from hand movement data (collected using background sensors like
accelerometer, gyroscope and magnetometer) and touch screen logs. Following the self- supervised learn-
ing protocol, MMFusion is pre-trained to capture similarities between hand movement sensor data and touch
screen logs, effectively attracting similar pairs and repelling dissimilar ones. Extensive evaluations demon-
strate its high performance on user verification across diverse tasks compared to unimodal alternatives trained
using the SimCLR framework. Moreover, experiments in semi-supervised scenarios reveal the superiority of
MMFusion with the best trade-off between sensitivity and specificity.
1 INTRODUCTION
Active biometric verification on mobile devices
consists in verifying the identity of a user fre-
quently (Stylios et al., 2021). In active biometric sys-
tems, samples are collected continuously, as an ex-
ample: Face, motion gestures, walking, typing, and
scrolling can be used for active biometric verification
on mobile devices (Stragapede et al., 2023) (Fathy
et al., 2015) (De Marsico et al., 2014). In a realistic
scenario, active biometric systems can combine mul-
tiple modalities with fusion at different levels (Stra-
gapede et al., 2022). Specifically, multimodal fusion
can be achieved with touch screen interactions and
hand movement. Mobile users produce touch screen
interactions while moving the device with their hands.
This typical way of mobile usage provides unique pat-
terns from touch screen and motion sensors for active
biometric verification (Sitov
´
a et al., 2016).
In the dynamic field of biometric research,
deep neural networks have attracted considerable
attention. Previous studies explored state-of-the-
art architectures, including Convolutional Neural
Networks (CNN) (Tolosana and Vera-Rodriguez,
2022) and Long Short-Term Memory Networks
(LSTM) (Tolosana et al., 2018) (Delgado-Santos
et al., 2022), focusing on behavioral biometric at-
tributes. While these architectures have delivered
valuable insights, there is a notable gap in the liter-
ature regarding the integration of multimodal data for
active biometric verification. In (Zou et al., 2020), the
authors demonstrated that Convolutional Recurrent
Neural Netwrok (CNN-RNN) can extract robust fea-
ture representations using inertial sensors for active
mobile verifcation. This work reported impressive
performance, achieving accuracy rates of 93.5% for
person identification and 93.7% for verification. Sim-
ilary, Giorgi et al. (Giorgi et al., 2021) introduced a
verification framewrok for mobile devices that lever-
aged gait patterns, combining inertial sensors with
a recurrent neural network. Their approach demon-
strated remarkable effectiveness in terms of biomet-
ric verification and real-time efficiency, substantiated
through a series of practical experiments. Recently,
Stragapede et al. (Stragapede et al., 2023) proposed
a verification system that relies on an LSTM model
and, importantly, incorporates modality fusion at the
score level. They reported consistent results, with
an Area Under the Curve (AUC) score ranging from
80% to 87% for random impostor verification and
an AUC score ranging from 62% to 69% for skilled
impostor verification. In a similar work (Stragapede
Ouadjer, Y., Galdi, C., Berrani, S., Adnane, M. and Dugelay, J.
Self-Supervised-Based Multimodal Fusion for Active Biometric Verification on Mobile Devices.
DOI: 10.5220/0012359000003654
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2024), pages 739-744
ISBN: 978-989-758-684-2; ISSN: 2184-4313
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
739

et al., 2022), the authors explored the fusion of touch
screen and motion sensors for active biometric veri-
fication while users engaged in various tasks such as
typing, scrolling, and tapping. Notably, they adopted
a weighted fusion strategy at the score level using
different sensors and demonstrated that the combina-
tion of keystroke touch data and magnetometer sensor
data consistently yielded the most favorable results
in terms of verification scores. Nevertheless, even
though these studies have made significant progress
in advancing active biometric verification, there is a
lack of research examining the fusion of multimodal
data. The unexplored potential of integrating multiple
sensory inputs in enhancing the accuracy and reliabil-
ity of biometric verification represents an important
path for future research.
In mobile biometrics, data annotation challenges
persist, primarily due to the sensitivity of biomet-
ric data. Self-supervised learning, such as con-
trastive pre-training, offers a solution by utilizing un-
labeled data for pre-training and fine-tuning on la-
beled data. We propose a self-supervised multimodal
fusion framework inspired from (Chen et al., 2020),
uniquely capitalizing on the rich sources of touch
screen data and sensor data. Leveraging unlabeled
data from these modalities, we construct feature rep-
resentations based on positive and negative pairs, al-
lowing for the effective transfer of these representa-
tions in the context of user verification.
The proposed multimodal fusion (MMFusion)
framework is trained on the Hand Movement
Orientation and Grasp (HMOG) database (Sitov
´
a
et al., 2016), and Human Interaction (HuMIdb)
database (Acien et al., 2021). The evaluation is
performed on different touch screen tasks such as:
Scrolling, tapping and swipping. The performance
of MMFusion is evaluated against the original con-
trastive learning (SimCLR) (Chen et al., 2020) us-
ing the hand movement modality and touch modal-
ity separately. Interestingly, the verification perfor-
mance of our MMFusion approach outperforms the
SimCLR approach with a significant margin. More-
over, the verification performance is assessed when
the MMFusion is finetuned on different fraction of la-
bels, demonstrating high performance using a small
set of labeled examples. We summarize our contribu-
tions as follows:
• We propose a self-supervised multimodal fusion
framework for active biometric verification by
combining touch screen and hand movement data.
• Performance of MMfusion framework is evalu-
ated on different touch screen tasks and compared
to the SimCLR approach with touch screen and
sensor modalities.
e)
Fine-tuning
d)
Contrastive learning
b)
Encoder module
a)
Multimodal data
c)
Concatenation + Fusion
Touch
Gestures
Feed
Fordward
Network
g(.)
Feed
Forward
Network
g(.)
Contrastive Loss
Input
Pairs
Motion
Gestures
Encoder 1
f(.)
Encoder 2
f(.)
h
(i)
h
(j)
x
(i)
x
(j)
h
(i)
h
(j)
h
(i)
h
(j)
z
(i)
z
(j)
Original features
Fused Features
Original features
Fused Features
Mapping
Mapping
Input
Pairs
Touch
Gestures
x
(i)
Motion
Gestures
x
(j)
Encoder 2
f(.)
Encoder 1
f(.)
h
(j)
h
(i)
h
(i)
Feed
Fordward
Network
g(.)
Feed
Forward
Network
g(.)
h(.)
Fused Features
y
(i)
y
(j)
Fine-tuning
Fine-tuning
Fused Features
Fully connected
layer
Score
Cross
entropy
loss
h
(j)
Figure 1: The proposed multimodal fusion framework. a)
Multimodal data: Data is sampled from touch and mo-
tion gesture modalities. b) Encoders: the encoders produce
features from motion and touch gesture inputs. c) Fusion:
features produced by the encoders are passed to the feed-
forward networks for fusion. d) Contrastive learning: both
original and fused features are used for computing similari-
ties and loss. e) Fine-tuning: the fully connected layer h(.)
is trained with labeled data.
• Extensive experiements conducted on two bench-
mark databases, report high verification perfor-
mance when both touch and hand movement
modalities are combined, in addition to the sce-
nario where the fusion model is fine-tuned on lim-
ited labeled data.
2 PROPOSED METHOD
In this section the proposed MMFusion approach
based on contrastive learning is described, follow-
ing different stages, from contrastive pre-training with
multimodal data to fine-tuning on limited labeled
training data.
2.1 MMFusion Framework
The proposed MMFusion is inspired from the work
of SimCLR (Chen et al., 2020), initially introduced
for representation learning in images. But the re-
ICPRAM 2024 - 13th International Conference on Pattern Recognition Applications and Methods
740

search community followed-up with multiple variants
of SimCLR, where different regularizations schemes,
and input modalities were used to learn similarities
from pairs of data (Jing and Tian, 2021). Figure 1
illustrates our solution, it is composed of five steps:
multimodal data, DNN encoder, fusion module, con-
trastive loss and finally fine-tuning. Further details
will be given about each component in the following
subsections.
2.1.1 Multimodal Data
The data module represents the first part of multi-
modal contrastive learning, in the original work of
SimCLR. (Chen et al., 2020) the authors proposed
data augmentation to generate similar input pairs from
a single modality, however in our case we simply ex-
tract pairs of input from different modalities i.e. touch
screen and hand movement modality. Following Fig-
ure 1.(a), each input pair is composed of two elements
(X
1
,X
2
) denoting the sensor and touch gesture vec-
tors of a given mobile user. The input feature vector
of hand movement modality S
q
is a sequence com-
posed of 3 inertial sensors, each one describing the ac-
celeration (a
x
,a
y
,a
z
), angular velocity (g
x
,g
y
,g
z
) and
ambient magnetic field (m
x
,m
y
,m
z
) using 3 physical
axes. It results in an input vector of 9 elements: S
q
= {a
x
,a
y
,a
z
,g
x
,g
y
,g
z
,m
x
,m
y
,m
z
}. Similarly the in-
put vector for the touch modality is a sequence T q =
{x,y, p,s} where x and y are the coordinates of the fin-
ger on the screen, while p and s denote the pressure
and surface of the finger on the mobile screen. The
trainig batch of positive pairs used during contrastive
learning is then expressed as follows:
D
P
= {(X
(1)
1
,X
(2)
1
),(X
(1)
2
,X
(2)
2
),...,(X
(1)
N
,X
(2)
N
)} (1)
The negative pairs of contrastive learning are sampled
following the equation2:
D
N
= {(X
(1)
i
,X
(2)
k
)}
i̸=k
, (2)
X
i
and X
k
are input feature vectors from two different
mobile users.
2.1.2 Encoder Module
The encoder module represented in Figure 1.(b), takes
the input feature vector of the sensor and touch
screen data, performs a series of linear and non-linear
transformations to map each feature vector (X
1
,X
2
)
into a representation h
1
and h
2
. The encoder mod-
ule is based on convolutional recurrent neural net-
work (Conv-RNN), proposed in (Tolosana and Vera-
Rodriguez, 2022). The architecture of the encoder
is based on two CNN layers with maxpooling in be-
tween, and two recurrent layers. Dropout is used after
the second convolutional layer, and the first recurrent
layer respectively. In the end, a linear layer is used to
produce representations which are then passed to the
fusion module.
h
(1)
= f (X
(1)
),
h
(2)
= f (X
(2)
)
(3)
2.1.3 Multimodal Fusion Module
The multimodal fusion module is a feed-forward neu-
ral newtork with two hidden layers, described by the
function g(.) in Figure 1.(c). This module takes as in-
put concatenated features (h
1
,h
2
) from touch screen
and sensor data, performs joint feature fusion with lin-
ear and non-linear operations to transform the features
into representations z
1
and z
2
.
z
(1)
= g(h
(1)
,h
(2)
),
z
(2)
= g(h
(2)
,h
(1)
)
(4)
2.1.4 Contrastive Task
Contrastive learning represents the final part of the
MMFusion module (Figure 1.(d)), the representa-
tions obtained after the fusion are used for contrastive
learning, following equation 5:
l
i, j
= − log
exp(sim(z
(i)
,z
( j)
)/τ)
∑
2N
k=1
[k̸=i]
exp(sim(z
(i)
,z
(k)
)/τ)
(5)
The loss l
i, j
is the cross-entropy loss computed for
similarities, and sim(z
(i)
,z
( j)
) is the cosine similarity
function between a pair of feature vectors:
sim(z
(i)
,z
( j)
) =
z
(i)⊤
z
( j)
τ∥z
(i)
∥.∥z
( j)
∥
(6)
• N: Number of samples in the training batch.
• ∥z
(i)
∥ and ∥z
( j)
∥: The ℓ
2
norms of the two feature
vectors z
(i)
and z
( j)
respectively.
• τ: Hyperparameter set to 0.5 in our case; to scale
the cosine similarity to a range of [-1, +1].
•
[k̸=i]
∈ {0,1}: Binary indicator which is equal to
: 1 if k ̸= i, and 0 if not.
The loss L is evaluated for (i, j) and ( j,i) pairs in
the positive training batch D
P
following Equation 7:
L =
1
2N
N
∑
k=1
[l(2k − 1,2k) + l(2k,2k − 1)] (7)
The final loss L
total
is computed for both original
and fused features as expressed in Equation 8. By
combining the original and fused features, the MM-
Fusion framework builds powerful representations to
discriminate between similar and dissimilar pairs of
multimodal data.
L
total
= L(z
i
,z
j
) + L(y
i
,y
j
) (8)
Self-Supervised-Based Multimodal Fusion for Active Biometric Verification on Mobile Devices
741

2.2 Fine-Tuning
In the fine-tuning step, MMFusion is trained on la-
beled training data following a user verification sce-
nario. Input pairs from the same user are considered
as genuine samples, while pairs from different users
are set to be impostor samples. Fine-tuning process is
illustrated in Figure 1.(e), the two encoders are used
as feature extractors and the prediction layer h(.) is
trained to output a probability score for genuine or
impostor class. The prediction network is composed
of a fully-connected layer with a sigmoid as an acti-
vation function.
3 EXPERIMENTAL EVALUATION
3.1 Databases
Two databases are used in this study, namely: the
Hand Movement Orientation and Grasp (HMOG)
database (Sitov
´
a et al., 2016), and the Human Ma-
chine Interaction database (HuMIdb) (Acien et al.,
2021). The data pipeline starts by segmenting raw
hand movement (accelerometer, gyroscope, magne-
tometer) data and touch data into 50% overlapping
time-windows of approximately 1 second length, for
the two databases, the training and validation sets are
generated by subjects.
HMOG database is a freely available database
proposed in the context of multimodal active user ver-
ification for mobile devices. It comprises hand move-
ment data (from background sensors: accelerometer,
gyroscope, magnetometer) and different touch ges-
ture interactions. A total of 100 users participated in
the data collection process, which makes it one of the
largest benchmark databases used in the litterature.
Data is splitted by selecting 80% of the subjects for
training and 20% of the remaining users for testing.
HuMIdb is a publiculy available database col-
lected with 14 sensors while users interacted natu-
rally with their mobile phones (Acien et al., 2021).
Compared to other databases, HuMIdb includes 600
users, it is the largest database up-to-date. In our
study, we have selected 100 users from the 600 cor-
pus, and we have used hand movement data (given by
three background sensors: accelerometer, gyroscope,
magnetometer) combined with touch gesture interac-
tions. Here also, data is splitted similarly to HMOG
database.
3.2 Implementation Details
Multimodal pre-training is conducted following the
framework of the Figure 1, separately, for HMOG and
HuMIdb database. The encoder f(.) and the feed for-
ward network g(.) are trained for 1000 epochs without
labeled training data using stochastic gradient descent
(SGD) and momentum of 0.9. Batch size is set to 128
and learning rate to 10
−3
. In order to compare the
performance of the MMFusion framewrok, we repli-
cated the original (unimodal) SimCLR (Chen et al.,
2020) framework by performing contrastive learning
with the hand movement modality and touch gesture
modality separately. The pre-training routine and hy-
perparameters used for SimCLR are the same as for
MMFusion. We used the Pytorch library and a GPU
NVIDIA GTX 1060 for pre-training routine.
In the fine-tuning phase, the encoder f(.) and the
fully connected layer h(.) are fine-tuned with labeled
training data for 200 epochs using SGD with a learn-
ing rate of 10
−2
. Similarly to MMFusion, we con-
ducted the same fine-tunning routine for the SimCLR
framework using hand movement modality and touch
modality.
3.3 User Verification
As explained in the section 2.2, MMFusion is eval-
uated on user verification, results are reported in Ta-
ble 1 for HMOG database, in terms of EER and AUC.
In the case of MMFusion and SimCLR applied to
the touch modality, AUC and EER scores are com-
puted for 3 different touch gesture tasks: Tapping,
scrolling and swipping. However, for the hand move-
ment modality, only the tapping task is used for com-
puting AUC and EER. This is due to the data collec-
tion process of HMOG, where sensor data is collected
once for all the touch gesture tasks.
Table 1: Results of user verification on HMOG database.
Modality Task
Performance
AUC (%) EER (%)
Fusion
Ours
Tap 97.48 10.71
Scrollling 83.62 28.95
Swipping 75.08 48.80
Sensor
SimCLR
Tap 93.25 13.2
Touch
SimCLR
Tap 93.56 12.11
Scrolling 82.21 26.91
Swipping 37.46 50.34
As it can be seen in Table 1, the proposed model
outperforms the hand movement modality and touch
modality on the different tasks. Specifically, using the
ICPRAM 2024 - 13th International Conference on Pattern Recognition Applications and Methods
742

(a) ROC curve on HMOG for 25% of labels. (b) ROC curve on HMOG for 50% of labels. (c) ROC curve on HMOG for 80% of labels.
(d) ROC curve on HumIdb for 25% of labels. (e) ROC curve on HumIdb for 50% of labels. (f) ROC curve on HumIdb for 80% of labels.
Figure 2: Receiver operating chracteristic curve of the three evaluated models: MMFusion, sensor model, and touch model.
All the models are fine-tuned on test set for three different fractions of labels: 25%, 50% and 80%.
tapping task, MMFusion outperforms the hand move-
ment modality by 4.23% and 2.49% margin on AUC
and EER respectively. MMFusion also outperforms
the touch modality by 3.92% margin on AUC and
1.4% on EER. Similarly for scrolling and swipping
tasks, the MMFusion framework is the best perform-
ing method. It is clear that the combination of touch
and sensor data brings significant improvement for
user verification compared to a single modality.
Table 2: Results of user verification on HuMIdb database.
Modality Task
Performance
AUC(%) EER (%)
Fusion
Ours
Scroll up 99.82 1.70
Scroll down 99.15 3.57
Tap 87.04 19.55
Swipe 64.92 38.60
Sensor
SimCLR
Scroll down 96.74 10.20
Scroll up 91.8 15.98
Swipe 81.38 29.58
Tap 81.17 29.76
Touch
SimCLR
Tap 95.90 10.26
Swipe 48.35 50.16
Results of user verification on HuMIdb are re-
ported in Table 2, the proposed multimodal fu-
sion outperforms the hand movement modality on:
scroll up, scroll down, and tapping tasks. The best
verification performance is obtained with scroll up
task, where MMFusion outperforms hand movement
modality by 8.02% margin on AUC and 14.28%
on EER. However, hand movement modality outper-
forms the proposed model on the swipe task, and
touch modality outperforms MMFusion on tap task.
This means that some touch gesture tasks add more
complexity to the fusion system, and would prefer-
ably be used in a single modality for user verification.
3.4 Semi-Supervised Evaluation
As it was stated before, collecting and labeling data
for biometric applications raises privacy concerns
because annotations include sensitive information.
Therefore to reduce the annotations, we evaluate the
performance of the proposed MMFusion on user veri-
fication when different fractions of annotated data are
available. We conduct experiments on MMFusion by
fine-tuning the model on three different fractions of
labeled data per class: 25%, 50%, and 80% of labels.
The same experiments are conducted using SimcLR
framework for sensor and touch gesture modality. For
each of the three models (MMFusion, sensor and
touch) evaluated on user verification, we selected the
best performing one in each task to perform semi-
supervised evaluation. Therefore, on HMOG data the
three models are selected according to the tapping
task. While for HuMIdb, MMFusion is selected ac-
cording to scrolling up task, the sensor model is se-
lected according to scroll down task and finally the
touch model is selected according to tapping task.
In the semi-supervised scenario, the encoder f(.)
and linear classifier h(.) are fine-tuned according to
Self-Supervised-Based Multimodal Fusion for Active Biometric Verification on Mobile Devices
743

the number of labels available for each configuration
(25%, 50%, 80%).
Resutls of the semi-supervised experiementation
are reported in Figure 2, interestingly, MMFusion
outperforms sensor and touch modality even on a
very limited portion of annotated train set of 25%
(Figure 2.(a)) by showing the best trade-off between
sensitivity and specificity. Moreover, we observe
consistent improvement with more annotated data
available, in the case of 50% and 80% of train la-
bels (Figure 2.(b) and Figure 2.(c)). Regarding Hu-
mIdb database, MMFusion gives the best trade-off
between sensitivity and specificity when it is fine-
tuned on 25% labels of train data (Figure 2.(d)), and
improvement is consistent with more available la-
bels (Figure2.(e) and Figure2.(f)). Unlike sensor and
touch modality, MMFusion shows the best trade-off
in semi-supervised evaluation, both on HMOG and
HumIdb databses, which makes it a feasible solution
when limited annotated data is used for fine-tuning a
self-supervised model.
4 CONCLUSIONS
In this paper a powerful multimodal fusion is pro-
posed within the context of active biometric verifica-
tion on mobile devices. It relies on self-supervised
learning, and combines touch screen and hand move-
ment data collected from mobile users while perform-
ing natural interactions. MMFusion builds strong fea-
ture representations at the contrastive learning level
by leveraging the complementary information of sen-
sor and touch data.
Extensive experiments on two benchmark
databases show that the proposed model outperforms
contrastive learning with SimCLR when applied on
hand movement and touch modality separately. In
addition, during semi-supervised evaluation where
labeled data is very limited, MMFusion gives the
best trade-off compared to hand movement and touch
models. In a future work, we aim to evaluate the
vulnerability of self-supervised models on active
biometric verification and suggest a method to defend
against adversarial attacks.
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