Smartphone based Finger-Photo Veriﬁcation using Siamese Network

Jag Mohan Singh, Ahmad S. Madhun, Ahmed Mohammed Kedir and Raghavendra Ramachandra

Norwegian Biometrics Laboratory, Norwegian University of Science and Technology, Gjøvik, Norway

Keywords:

Fingerphotos, Siamese Neural Network, Deep Learning Architecture.

Abstract:

With the advent of deep-learning, ﬁnger-photo veriﬁcation, a.k.a ﬁnger-selﬁes, is an upcoming research area

in biometrics. In this paper, we propose the Siamese Neural Network (SNN) architecture for ﬁnger photo

veriﬁcation. Our approach consists of a MaskRCNN network used for ﬁnger photo segmentation from an input

video frame and the proposed Siamese Neural Network for ﬁnger-photo veriﬁcation. Extensive experiments

are carried out on the public dataset consisting of 400000 images extracted from 2000 videos in ﬁve different

sessions. The dataset has 200 unique ﬁngers, where each ﬁnger is captured in 5 sessions, 2 sample videos each

with 200 frames. We deﬁne protocols for testing in the same session and different sessions with/without using

the same subjects replicating the real-world scenario. Our proposed method achieves an EER in the range of

8.9% to 34.7%. Our proposed method does not use COTS and uses only a deep neural network.

1 INTRODUCTION

Smartphone biometrics usage increases with time,

attributed to high-quality smartphone cameras, in-

creased compute capability, and dedicated sensors on

smartphones. The broader use of smartphone bio-

metrics is due to their portability, cost-effectiveness,

and growing consumer market acceptance (Das et al.,

2018). With the advent of smartphone-banking ap-

plications such as Apple-Pay (App, ), it is essential

to have biometric authentication-based solutions in

them (Stokkenes et al., 2018). Traditionally biomet-

ric authentication is performed using face, iris, and

ﬁngerprint modalities. Apple Face ID (App, 2017), &

Touch ID (App, 2013) are used for face and ﬁnger-

print authentication on an iPhone, which provides a

high level of accuracy. However, these require ded-

icated sensors, which are an additional cost to the

smartphone manufacturer.

Recently, Finger-Photo, a.k.a 2D touchless ﬁnger-

print, a new modality for biometric veriﬁcation, has

emerged due to its direct usage with a smartphone

camera. Finger-Photo veriﬁcation is an active re-

search area, as pointed out in a recent survey by Busch

et al. (Priesnitz et al., 2021). Malhotra et al. (Malho-

tra et al., 2017) performed a short survey on Finger-

Photo recognition with the smartphone as a capture

device. Labati et al. (Labati et al., 2019) conducted

a more detailed survey on ﬁngerprint recognition sys-

tems, including their weakness and challenges. The

main challenges in Finger-Photo veriﬁcation systems

based on smartphones are sensor-to-ﬁnger distance,

sharpness, quality, and focus, which can be alleviated

using preprocessing, the region of interest (ROI), and

a robust color space (Priesnitz et al., 2021). Stein et

al. (Stein et al., 2012) had one of the early works in

ﬁnger photo recognition. They developed an Android

Application for this purpose which took a sequence of

ﬁnger photo images in multiple lighting conditions,

followed by Region of Interest (ROI) extraction and

binarization. The matching scores on binarized im-

ages were computed by open-source minutae extrac-

tor FingerJetFXOSE from Digital Persona (Persona,

2020). However, this method was unable to handle

defocussed ﬁnger photo images. Lee et al. (Lee et al.,

2005b) by a real-time scheme that took the most fo-

cussed image from an image sequence addressed the

issue of sharpness during ﬁnger-photo capture and

was achieved by using Variance-Modiﬁed-Laplacian

of Gaussian (VMLOG) algorithm. The issue of con-

trast, and color which is an important issue, has been

addressed by many authors. Lee et al. (Lee et al.,

2005a) handled this issue by using skin color proper-

ties & guided machine learning. The main limitation

of their approach was that they required user input

for Finger-Photo segmentation. Malhotra et al. (Mal-

hotra et al., 2017) handled this by using the ma-

genta channel in CMYK color space. Raghavendra et

al. (Raghavendra et al., 2013) proposed an approach

using the Mean Shift Segmentation (MSS) based sys-

Singh, J., Madhun, A., Kedir, A. and Ramachandra, R.

Smartphone based Finger-Photo Veriﬁcation using Siamese Network.

DOI: 10.5220/0010880000003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages

553-559

ISBN: 978-989-758-555-5; ISSN: 2184-4321

553

Figure 1: Illustration showing conversion of Finger-Photo

to Fingerprint like pattern using Frangi (Frangi et al., 1998)

used by Wasnik et al. (Wasnik et al., 2018).

tem. They used multiple features after MSS consisted

of preprocessing and scaling to segment the ﬁnger in

challenging real-world environments accurately.

Wasnik et al. (Wasnik et al., 2018) used the

Frangi Vesselness ﬁlter (Frangi et al., 1998) ﬁlter

to convert a Finger-Photo to a ﬁngerprint-like pat-

tern as shown in Figure 1. However, their method

requires commercial-off-the-shelf (COTS) for veriﬁ-

cation. Malhotra et al. (Malhotra et al., 2020) pro-

posed the use of Invariant Scattering Networks from

Bruna et al. (Bruna and Mallat, 2013). The input to

their method is a full-frame ﬁnger photo which was

then segmented using a weighted combination of vi-

sual saliency (Erdem and Erdem, 2013) followed by

OTSU thresholding (Otsu, 1979), and Skin Colour

based segmentation (Sawicki and Miziolek, 2015).

This is followed by computing wavelet-like features

from the second-order decomposition of the cropped

ﬁnger photo image (Bruna and Mallat, 2013). The di-

mension of these features is very high 209×width/8×

height/8 = 809875, for which they use PCA (Jol-

liffe, 1986) to reduce the dimension to 99% of its en-

ergy. This is followed by the use of Random Decision

Forests (Ho, 1995).

1.1 Contributions of the Paper

We summarize the contributions of the proposed ap-

proach:

• Finger-Photo segmentation is a challenge for most

of the presented methods. This issue is not en-

tirely resolved in previous methods as some part

of the background which acts as noise for the

matching algorithm is present. We handled this

issue by the Mask RCNN based Finger-Photo seg-

mentation and produced a tightly cropped Finger-

Photo. We require only a loosely cropped bound-

ing box as an input to the Mask RCNN network.

• We provide an end-to-end approach for Finger-

Photo veriﬁcation, including segmentation and

matching. The matching is done by the use of

the proposed siamese neural network. This is an-

other contribution of this paper, as most previous

methods depend on either COTS or existing open-

source algorithms.

• The proposed approach uses the proposed con-

volutional neural (SNN) decisions directly as the

classiﬁcation labels.

• We deﬁne challenging protocols for matching to

simulate the real-world scenario where the data

environment or subjects are not seen by the ver-

iﬁcation algorithm beforehand.

In the rest of the paper, we present the proposed

method in Section 2, describe the experimental setup

& results in Section 3, and conclude the paper by pro-

viding conclusions & future work in Section 4.

2 PROPOSED METHOD

In this section, we describe the proposed method as

shown in Figure 2. The proposed method consists

of the following components, Finger-Photo segmen-

tation & cropping for a pair of input images, feature

extraction with the proposed Siamese Network-based

Architecture for ﬁnger photo veriﬁcation, and then

classiﬁcation. We now describe each of these com-

ponents in the following sub-sections:

2.0.1 Finger-photo Segmentation & Cropping

We describe the approach for ﬁnger-photo segmenta-

tion in this subsection. The public dataset consists of

ﬁnger videos from which frames are extracted. This

is followed by bounding box-based cropping, where

a loose bounding box with some extra region is de-

ﬁned on the once for the dataset basis. The bound-

ing box crop is followed by Finger-Photo segmenta-

tion using ﬁne-tuned MaskRCNN Network from He

et al. (He et al., 2017). Then we apply a Region of

Interest (ROI) on the segmented Finger-Photo. The

stages are shown in Figure 2. This approach’s main

advantage is that the user does not need to do either

ﬁner level of cropping or bounding box generation for

a single Finger-Photo (Wasnik et al., 2018) (Lee et al.,

2005a).

2.0.2 Classiﬁcation using Proposed Siamese

Architecture based Network

Feature Extraction uses Siamese Neural Network Ar-

chitecture proposed by Chopra et al. (Chopra et al.,

2005) for Face Veriﬁcation which consists of two con-

volution neural networks with shared weights. Our

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

554

Fingerphoto

Segmentation &

Cropping for a pair

Classification using

Proposed Siamese

Architecture based

Network

Full Frame Cropped Segmented ROI

Fingerphoto Left

(224x208)

Fingerphoto Right

(224x208)

Resnet-50 (Pre-trained)

(activation 49) (2048)

Flatten

Dense (relu) (1024)

Dense (sigmoid) (128)

Euclidean Distance Layer

Resnet-50 (Pre-trained)

(activation 49) (2048)

Flatten

Dense (relu) (1024)

Dense (sigmoid) (128)

Classification (Genuine/Impostor)

Figure 2: Illustration showing system diagram of the proposed approach, where ﬁrst block shows ﬁnger segmentation &

cropping, and second shows proposed siamese network based architecture.

proposed siamese network has the architecture as

shown in Figure 2. It consists of the identical layers

for both left, & right halves, and each half includes

a Resnet-50 as the backbone network with an image

dimension of 208 × 224. The output is taken at the

last activation of 2048 dimensions connected to our

custom layers of ﬂattening, a dense layer (1024 di-

mensions with ’relu’ activation), a dense layer (128

dimensions with sigmoid activation). A distance layer

(’euclidean’ distance) is then used. We choose Resnet

as the base network as it offers good generalization

according to He et al. (He et al., 2020), and Resnet-

50 because it provides a balance between computing

required, & accuracy. We further use only a small

number of layers for ﬁne-tuning as the number of im-

ages we use in training are much lower than Imagenet

dataset (Deng et al., 2009). The loss function used

during training is binary cross-entropy, mainly chosen

as a balanced two-class classiﬁcation problem. The

features for each Finger-Photo are computed as the

output from the dense layer of 128 dimensions. The

features are extracted from the ﬁnal dense layer of

128 dimensions, and euclidean distance is computed

between them to perform classiﬁcation. We then ap-

ply a threshold of 0.5 to classify the input pair of ﬁn-

gers as genuine or impostor. We use a classiﬁer-less

system, and our proposed approach does not require

other classiﬁers after using the proposed network.

3 EXPERIMENTAL SETUP &

RESULTS

This section describes the training methodology, re-

sults, and a discussion on the obtained results.

3.1 Dataset Details

We now describe the generation of training, val-

idation, and testing pairs. We use the public

dataset (Raghavendra et al., 2020) for training and

evaluating our proposed method. The dataset consists

of 200 unique ﬁngers, where each ﬁnger is captured

in 5 sessions, 2 sample videos with 200 frames each.

Thus, overall number of frames in the current dataset

uses is 200 × 5 × 2 × 200 = 40000 which is summa-

rized in Table 1.

3.2 Protocol Details

3.2.1 Training Mated/Non-mated Pairs

We include 100 subjects for training, and for training

mated pairs, a total of 50 frames are selected from

the two video samples, three pairs for each frame.

The frame number is the same in the ﬁrst pair, and

in the second & third pairs, the frame numbers are

Smartphone based Finger-Photo Veriﬁcation using Siamese Network

555

Figure 3: Illustration showing training accuracy, and loss for the proposed siamese based network.

Table 1: Table showing dataset details.

Dataset Details

Subjects Fingers Sessions Sample Videos Total Frames

50 4 5 2 40000

Table 2: Table showing training, validation, and testing partitions.

Training Mated/Non-Mated Details

Category Subjects Frames Samples Total Pairs Actual Pairs Used

Training Mated 100 50 3 15000 13800

Training Non-Mated 100 2 1 18000 16744

Validation Mated/Non-Mated Details

Category Subjects Frames Samples Total Pairs Actual Pairs Used

Validation Mated 100 25 3 7500 6900

Validation Non-Mated 100 1 1 9900 8372

Testing Mated/Non-Mated Details

Category Subjects Frames Samples Total Pairs Actual Pairs Used

Testing Mated 100 20 3 6000 5760

Testing Non-Mated 100 1 1 9900 9120

chosen randomly between 0 to 50. This makes the

total number of mated pairs 50 × 3 × 100 = 15000,

which due to failure to extract from Mask RCNN of

some samples is 13800. For training non-mated pairs,

we choose all pairs of ﬁngers (100×99, with multiple

pairs for each selected pair of a ﬁnger (2), we thus get

100 × 99 × 2 = 18000, which due to failure to extract

from Mask RCNN of some samples is 16744.

3.2.2 Validation Mated/Non-mated Pairs

For validation mated pairs we select 25 frames, and 3

pairs, giving (100 × 25 × 3 = 7500, with 6900 actual

pairs), and validation non mated pairs, we select 1 pair

giving (100 × 99 = 9900, with 8372 actual pairs).

3.2.3 Testing Mated/Non-mated Pairs

We include the rest 100 subjects for testing, and for

testing mated pairs we select 20 frames, and 3 pairs

for each frame, which makes the total number of test-

ing mated pairs 100 × 20 ×3 = 6000 with 5760 actual

pairs, and testing non mated pairs are 100×99 = 9900

with 9120 actual pairs.

3.3 Evaluation Protocols

• TD1: Testing on the Same Session, with Unseen

Reference and Probe Subjects.

This protocol uses the 100 subjects from the test-

ing partition to evaluate the proposed method,

which gives S2 vs. S2.

• TD2: Testing on a Different Session, with Seen

Reference and Unseen Probe.

In this protocol, we use the 100 subjects used dur-

ing training where reference is from the training

session 2, and a probe is from unseen session 2 to

6, which gives S2 vs. S3, S2 vs. S4, S2 vs. S5,

and S2 vs. S6.

• TD3: Testing on Different Session, with Unseen

Reference and Unseen Probe.

In this protocol, we use the 100 subjects from the

testing partition where reference is from the test-

ing partition of session 2, and the probe is from

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

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Table 3: EER of the built models.

Sessions

Error Equal Rate (EER)

Baseline Baseline + DA Baseline + DN

TD1 S2 vs S2 13.06% 8.95% 17.70%

TD2 S2 vs S5 33.68% 32.75% 20.53%

TD3 S2 vs S5 41.50% 34.71% 30.99%

TD4 S4 vs S4 11.44% 10.57% 17.44%

TD4 S5 vs S6 42.59% 30.27% 29.57%

unseen session 3 to 6, which gives S2 vs. S3, S2

vs. S4, S2 vs. S5, and S2 vs. S6.

• TD4: Testing on Different Session, with Unseen

Reference and Unseen Probe.

In this protocol, we use the 100 subjects which

are from the testing partition where reference is

from the testing partition of session 3 to 6, and

the probe is from unseen session 3 to 6, where we

choose S3 vs. S3, S4 vs. S4, S5 vs. S5, S5 vs. S6,

and S6 vs. S6.

3.4 Training Methodology

We now describe the ﬁnger photo segmentation per-

formed using Mask RCNN post the bounding box-

based crop. Mask RCNN network is ﬁne-tuned us-

ing 40000 manually segmented images using the Mat-

lab Image Segmenter Tool. The ﬁne-tuning is per-

formed for 100 epochs on a standard laptop. The

proposed Siamese-based network is trained for 100

epochs on NVidia Tesla P40 GPU using SGD opti-

mizer and learning rate of 0.001, the momentum of

0.9, and weight decay 0.1 with Nesterov solver. The

loss curve obtained during the training is shown in

Figure 3. The accuracy shows overﬁtting in the cur-

rent network design, but the fact validation accuracy

is close to 96% is helpful in an uncontrolled scenario.

3.5 Results

The error metrics are used in accordance

with (ISO/IEC TR 2382-37:2012, 2012) where

False Match Rate (FMR) is the number of non-mated

comparisons which result in a false match, and False

Non-Match Rate (FNMR) is the number of mated

comparisons which result in false non-match. The

plot of FMR v/s FNMR is the DET (Detection Error

Tradeoff) Curve and EER (Equal Error Rate), which

is the threshold where FMR equals FNMR. The DET

Curves are shown in Figure 4. DET Curve is the

False Match Rate (FMR) v/s False Non-Match Rate

(FNMR) plot. Table 3 shows EER for a few different

cases from different protocols.

The EER is shown in tabular form in Ta-

ble 3, and as DET Curves in Figure 4. We per-

form data augmentation (DA) over the baseline pro-

posed method. The augmentation techniques in-

clude increasing/decreasing brightness, sharpness, or

Gaussian noise to samples randomly during train-

ing/testing, which improves performance as shown

in Table 3. In terms of data normalization, we use

Frangi Filter output images instead of ROI images in

the baseline model, which further improves many pro-

tocols’ performance.

3.6 Analysis of Results

The performance difference of the proposed method

with & without data augmentation/normalization is

analyzed as follows:

• The proposed approach provides an end-to-end

system for score computation and is currently not

robust to illumination, brightness, and contrast

changes as the baseline EER is high. However,

this is alleviated to a certain extent by the use

of data augmentation techniques which make it

slightly robust to these changes.

• The proposed approach, when used in combina-

tion with data normalization based on the gen-

eration of ﬁngerprint-like patterns which are ob-

tained using Frangi ﬁlter (Frangi et al., 1998) re-

sults in lower error rates.

• The performance of the proposed approach & DA

achieves a low EER of 8.95% when both the train

& test session are the same (TD1). This is mainly

due to the fact as transformations of illumination,

brightness, and contrast are low.

• The performance for an unseen session during the

testing is lowest for baseline + DN, where we

achieve an EER of 20.53% for TD2. This can be

attributed to DA., and DN doesn’t generalize to

the unseen testing session scenario.

• The current feature extraction technique has is-

sues in generalization to unseen training data, at-

tributed to different capture conditions including

illumination, contrast, and brightness.

Smartphone based Finger-Photo Veriﬁcation using Siamese Network

557

(a) TD1: S2 vs S2 (same session, with

unseen reference and probe subjects)

(b) TD2: S2 vs S5 (different ses-

sion, with seen reference and unseen

probe)

sion, with unseen reference and un-

seen probe)

(d) TD4: S4 vs S4 (different ses-

sion, with unseen reference and un-

seen probe)

(e) TD4: S5 vs S6 (different ses-

sion, with unseen reference and un-

seen probe)

Figure 4: DET Curves for different evaluation protocols of the proposed method.

4 CONCLUSIONS &

FUTURE-WORK

In this paper, we presented an end-to-end system for

ﬁnger photo veriﬁcation. The method presented in

this paper has several advantages over previous tech-

niques. The ﬁrst is the use of MaskRCNN for ﬁnger

photo segmentation, which allows the user to have a

loose bounding box, unlike previous approaches. The

second advantage is that system behaves as an end-

to-end system without the use of COTS for veriﬁca-

tion. We performed an extensive evaluation for both

seen and unseen session scenarios. We would make

the network robust to noise, illumination, and qual-

ity changes in the input images, especially to achieve

good performance in an unseen testing session. This

could be achieved in multiple ways, such as using

different color spaces that are more robust to these

changes, such as CMYK or CIE Lab. The robustness

to an unseen testing session can be improved by using

more data augmentation & data normalization tech-

niques. We want to compare our proposed approach

with more methods in SOTA (Malhotra et al., 2020)

& datasets, and especially for a cross-dataset scenario

in future work.

ACKNOWLEDGMENT

This work is carried out under the partial funding of

the Research Council of Norway (Grant No. IKT-

PLUSS 248030/O70).

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