=Paper=
{{Paper
|id=Vol-2843/paper32
|storemode=property
|title=Neural network biometric cryptography system (paper)
|pdfUrl=https://ceur-ws.org/Vol-2843/paper032.pdf
|volume=Vol-2843
|authors=Alexey Vulfin,Vladimir Vasilyev,Andrey Nikonov,Anastasia Kirillova
}}
==Neural network biometric cryptography system (paper)==
https://ceur-ws.org/Vol-2843/paper032.pdf
Neural network biometric cryptography system *
Alexey Vulfin, Vladimir Vasilyev, Andrey Nikonov and Anastasia Kirillova
Ufa State Aviation Technical University, 12, K.Marks st, Ufa, 450077, Russian Federation
nikonovandrey1994@gmail.com
Abstract. In this paper, an approach to the construction of a neural network
system of biometric authentication is proposed, which allows organizing the
distributed stor-age of the base of biometric images and using a secret crypto-
graphic key generated on the basis of the input biometric image as an output of
the neural network. The object of the research is the biometric authentication
system, and the subject of the research is the algorithms for converting parame-
ters into a cryptographic key based on neural network technologies. The struc-
ture of a biometric authentication system has been developed, which identifies
biometric features of a face image. The main difference between the developed
system and existing solutions is the method of constructing a vector of primary
biometric features based on neural network models and methods of machine
learning and data mining, which allows assigning a unique private crypto-
graphic key to each authentication subject. The mechanism of a distributed neu-
ral network representation of private key components significantly reduces the
likelihood of compromising the vector of biomedical features. The use of the
developed system and algorithms will make it possible to create highly reliable
biometric security systems that ensure the ability of users to work with confi-
dential information in open and weakly protected information systems.
Keywords: Information security, Neural network, Biometric, Cryptography,
Image analysis.
1 Introduction
A promising trend in increasing the efficiency of authentication systems is the inte-
gration of biometric and cryptographic methods in the task of converting biometric
parameters into an access key code (cryptographic key). The use of initial biometric
features for the generation of cryptographic keys has a number of difficulties: biomet-
ric data is not clearly reproducible and does not have a uniform distribution of pa-
rameters, while most cryptographic transformations are bijective and require an exact
key value [1–7].
*
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribu-
tion 4.0 International (CC BY 4.0).
Biometric authentication systems, that using machine learning models, approxi-
mate a set of multidimensional dividing hyperplanes in the space of selected features
of biometric templates, which makes it possible to isolate the templates of each prede-
fined class associated with the authentication subject. Systems whose output vector
reproduces the code of a predetermined class are vulnerable to attacks on the “last bit”
of the decision rule [8-9]. The use of neural network models in the core of the authen-
tication system is also associated with a number of disadvantages associated with the
need to retrain the neural network when adding a user and potential errors of the sec-
ond kind.
The goal of the research is improvement of biometric authentication algorithms
due to neural network transformation of biometric features into a cryptographic key.
To achieve the goal, the following tasks were set:
─ development of the structure of a neural network biometric authentication system
with the transformation of the vector of biometric features into a cryptographic pri-
vate key;
─ development of an algorithm for converting input biometric features into a crypto-
graphic “private” key in a neural network basis;
─ comparative analysis of the effectiveness of biometric authentication systems
based on machine learning models.
2 Materials and methods
The algorithm of the neural network biometric authentication system with the trans-
formation “biometrics – code” (NNBA) includes five stages (Figure 1) [10–20].
Fig. 1. Basic algorithm for neural network transformation of the original biometric features
into a cryptographic private key.
Image preprocessing. Image preprocessing performed as extraction of biometric
features from a “raw” biometric image. To train neural networks, a sample of data
was created with images of the faces of five users, which are presented in Figure 2.
Image preprocessing is performed according to the diagram in the Figure 3.
5000 images from the video stream were selected for each of the classes. The se-
lected areas have been labeled and scaled to 256x256 pixels to contain the minimum
number of pixels outside the face of interest. Additionally, the images were normal-
ized by equalizing the histogram to normalize certain sections of frames with different
brightness. Also for some neural networks, color images were converted from a color
scheme (RGB) to grayscale with a 255-bit palette (grayscale). The negative sample
consists of 5000 images, also reduced to a size of 64x64 pixels.
Fig. 2. Sample images from the training set.
Fig. 3. Scheme of the image preprocessing block.
Coding and selection of features for building a base of biometric images. At the
preprocessing stage, two training samples are prepared: positive and negative. A
“positive” training set consists of preprocessed images extracted from the video
stream and containing the user’s face (the subject of authentication). The “negative”
training set includes arbitrary images that do not contain fragments of a human face.
A sufficiently large number of examples in a “negative” training set in comparison
with a “positive” one allows the classifier model to use a larger number of images for
constructing dividing surfaces in the feature space and has a beneficial effect on the
learning outcomes of such models.
It is proposed to coding features using the following steps:
─ images of all classes of positive and negative samples are represented as an integer
matrix of uniform size [n, n], in which each pixel is an integer in the range [0,
255], which corresponds to the representation of the image in grayscale;
─ the unified matrix is split line by line, and a column vector of size [n*n, 1] is con-
structed. This step provides an invariant to displacement of the region of interest
(ROI) in the vertical direction;
─ each element of the column vector can be transformed into a reflective binary 8-bit
Gray code [21], and then the resulting matrix is again split into rows. A column
vector of size [8 * n * n, 1] is formed again from the received rows. Representation
of features in the form of Gray codes is due to the fact that two adjacent values of
the color scale differ only in one bit.
Feature generation consists in projecting the primary vector into a new feature
space and forming a compact feature vector of each image for subsequent neural net-
work processing. A binary or integer vector is fed to the input of a neural network
unit, which implements a functional mapping of an image into a unique vector, which
acts as a private cryptographic key.
It is proposed to use the following approaches for features generation:
Self-organizing two-dimensional Kohonen map (SOM). Figure 4 shows maps of
clustering of the feature vectors: user classes are displayed in yellow shades, a noisy
samples are displayed in red. The vectors of user attributes are visually divided into
three and five groups, which corresponds to the number of users in the system in the
first experiment on field data.
Probabilistic principal component analysis (PPCA). The input of the PPCA [22]
algorithm is an array with data and the value of the space dimension to which the data
should be “compressed”.
Figure 5 shows an example of highlighting two and three main components. Each
point corresponds to the image of the recognition object, in our case – the NNBA
users.
a– a
first experiment with 3 classes – first experiment with 5 classes
Fig. 4. Kohonen map with highlighted clusters in the first computational experiment.
a – 3 main components
a – 2 main components
Fig. 5. An example of the selection of compact groups of images based on the use of two and
three main components in the image.
Thus, it is proposed to use the first n = 16 ... 32 distinguished main components as
a compact vector of features of a human face.
Convolutional neural network (CNN). The outputs of the fully connected layer fc8
of the AlexNet convolutional network [23], consisting of 1000 neurons, providing the
integration of information about the facial features of a particular user, are used as a
compact vector of features.
Creation of a base of images “biometrics – cryptographic key”. For each user of
the system, whose images are used to extract biometric features, it is necessary to
match a previously generated cryptographic key.
The private key is represented as an integer column vector [m, 1] of length m =
132. Each element of the vector is converted to a reflexive binary 8-bit Gray code.
The rows of the resulting matrix are converted to a column vector [8 * m, 1]. This
transformation is shown in Figure 6.
For each class of input images of a positive sample, which are facial images of a
unique user, a single binary output vector is assigned, which is an encoded private
key.
For each negative sample, a random private key in binary representation is as-
signed.
One of the following options is used as an input vector:
─ image as an integer column vector;
─ binary representation of the image as an integer column vector;
─ binary vector formed by the output neurons of the two-dimensional Kohonen map,
the input of which was an image in the form of an integer column vector;
─ real-valued vector of activations of the fc8 layer of the AlexNet convolutional net-
work, to the input of which a color image [227, 227, 3] of a person’s face was fed
in the RGB palette;
─ real-valued vector formed by the principal components of the corresponding input
image after projection using the PPCA method.
Fig. 6. Converting private key to Gray code.
Thus, the training sample is a set of pairs of input and output vectors.
Neural network matching of compact vectors of biometric images to the cryp-
tographic keys. Matching of compact vectors of biometric images to the crypto-
graphic keys is implemented using the following methods:
─ use of bidirectional associative memory (BAM) [24];
─ single-layer and multilayer perceptrons.
The algorithm for constructing an extended BAM is shown in Figure 7.
Fig. 7. Algorithm for constructing extended BAM.
Key recovery upon presentation of a biometric image. The final structure of the
authentication system with neural network conversion of biometric parameters into a
cryptographic “private” key is shown in Figure 8.
Fig. 8. The structure of a neural network biometric authentication system.
3 Results
To assess the efficiency of the neural network transformation of the initial biometric
features into a cryptographic private key, several computational experiments with
using following models were carried out on field data (Table 1):
─ multilayer perceptron (MP);
─ PPCA and multilayer perceptron;
─ two-dimensional Kohonen map and multilayer perceptron;
─ convolutional neural network AlexNet and multilayer perceptron;
─ BAM based on B. Kosko’s neural network in the version of Y. Wang.
Table 1. computational experiments.
Experiment
Parameters 1 2 3 4 5
MP PPCA + MP Kohonen map + MP AlexNet + MP BAM
[64, 64, 1] [64, 64, 1] [64, 64, 1] [227, 227, 3] [64, 64, 1]
Source images
in RGB palette
No 64 principal 2D Kohonen map, AlexNet (fc8 No
Compact feature
components by 15*15 neurons with layer activa-
vector generation
PPCA a hexagonal grid tion neurons)
The architecture of the neural network matching unit
Dimension of the 4096 64 225 1000 4096
input vector
Input vector type Decimal integers Real numbers Binary vector of Real numbers Binary Gray
[0, 255] activities of neurons code
in the output layer of
the Kohonen map
Dimension of the 132 or 1056 1056 1056 1056 1056
output vector
Output vector Decimal integers binary Gray binary Gray code binary Gray binary Gray
type [0, 255] or binary code code code
Gray code
Neural netwok MP MP MP MP BAM
Number of 4096, 2018, 1056 64, 1056 225, 1056 1000, 1056 4096, 1056
neurons by layers
Activation func- elliotsig, elliotsig, satlins elliotsig, satlins elliotsig, satlins, satlins
tions of neurons elliotsig, satlins satlins
Post-processing no / hardlim hardlim hardlim hardlim hardlim
in network output
The results of experiments on the training set are shown in the Table 2.
Table 2. Training results.
Training
1 2 3 4 5
Parameters
PPCA + Kohonen
MP AlexNet + MP BAM
MP map + MP
Absolute number
394 362 281 273 23
of errors / exam-
[4500] [4500] [4500] [4500] [450]
ples
Proportion of
correctly recog- 91.24 91.96 93.76 93.93 94.89
nized images,%
Sensitivity 0.9688 0.9825 0.9784 0.9729 0.9865
Specificity 0.9727 0.9753 0.9830 0.9811 0.9628
Positive predic-
0.8794 0.8869 0.9188 0.9101 0.8391
tive value
Predictive value
of negative re- 0.9934 0.9965 0.9957 0.9946 0.9972
sults
Table 3 shows the results obtained during testing on field data.
Table 3. Test results on field data.
Training
1 2 3 4 5
Parameters
PPCA + Kohonen AlexNet +
MP BAM
MP map + MP MP
Absolute number of 133 124 95 88 8
errors / examples [1500] [1500] [1500] [1500] [150]
Proportion of correctly
91.13 91.73 93.67 94.13 94.67
recognized images,%
Sensitivity 0.9828 0.9806 0.9808 0.9808 1
Specificity 0.9763 0.9775 0.9774 0.9774 0.9758
Positive predictive value 0.8837 0.9004 0.9011 0.9014 0.8966
Predictive value of nega-
0.9968 0.9959 0.9959 0.9959 1
tive results
4 Discussion
The main advantage of the Kohonen maps according to the results of experiments is
the best scores of the first kind error. However, to add a new authentication subject, it
is necessary to retrain the neural network. The PPCA method performs the transfor-
mation of the feature vector without the need for a continuous learning process. The
use of convolutional neural networks allows to achieve the best sensitivity and speci-
ficity by extracting complex features from the original images, but training such a
network requires significant computing resources.
To match the compact vector of features isolated from the biometric image to the
private cryptographic key, multilayer perceptrons and hetero-associative memory
based on the BAM neural network were used.
The use of a neural network implementation of BAM makes it possible to effec-
tively implement the mechanism of functional mapping of the feature vector into a
private cryptographic key. However, due to the high dimensionality of the input fea-
ture vector and the large number of pairs of compared input (n) and output (p) vec-
tors, the total memory capacity is
m min(n, p ) (1)
For feedforward neural networks, one hidden layer is sufficient to match input
biometric images and output private keys. Testing the system on a control set of ex-
amples demonstrated the possibility of obtaining random output vectors for images of
users who are not subjects of authentication. If we refuse the use examples of negative
sampling when training the neural network core of the system, 42% of examples of
images of users who are not subjects of authentication are assigned by the system to
one of the available classes.
5 Conclusion
The paper proposes an approach that allows combining a biometric authentication
system module based on a machine learning model and a cryptographic module. The
neural network core of the system allows to organize the distributed storage of bio-
metric templates and implements the mapping of the input image into the generated
private cryptographic key.
The structure of a neural network system for biometric authentication has been de-
veloped, including a digital video stream processing module for extracting an image
of the authentication subject’s face. A distinctive feature is the way to transform the
vector of primary biometric signs into a compact vector using a self-organizing Ko-
honen map, a convolutional network, a probabilistic principal component algorithm,
bi-directional hetero-associative memory, and a multilayer neural network. The next
neural network unit implements the process of functional mapping of a compact vec-
tor of subject features into a unique private cryptographic key. Thus, it is possible to
organize distributed compact storage of the base of biometric images and reduce the
probability of compromise, since the whole process takes place in a neural network
basis, which is a “black box”.
Application of this approach will make it possible to create highly reliable biomet-
ric security systems that provide the ability for users to work with confidential infor-
mation in open and weakly protected information spaces.
6 Acknowledgments
The reported study was funded by Ministry of Science and Higher Education of the
Russian Federation (information security) as part of research project № 1/2020.
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