=Paper=
{{Paper
|id=Vol-2992/icaiw_waai_3
|storemode=property
|title=Predictive modeling toward identification of sex from lip prints - machine learning in cheiloscopy
|pdfUrl=https://ceur-ws.org/Vol-2992/icaiw_waai_3.pdf
|volume=Vol-2992
|authors=Agustín F. Sabelli,Parag Chatterjee,Ma Florencia Pollo-Cattaneo
|dblpUrl=https://dblp.org/rec/conf/icai2/SabelliCC21
}}
==Predictive modeling toward identification of sex from lip prints - machine learning in cheiloscopy==
https://ceur-ws.org/Vol-2992/icaiw_waai_3.pdf
Predictive modeling toward identification of sex
from lip prints - machine learning in cheiloscopy
Agustín F. Sabelli, Parag Chatterjee and Ma Florencia Pollo-Cattaneo
Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS), Universidad Tecnológica Nacional Facultad
Regional Buenos Aires, Buenos Aires, Argentina
Abstract
Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips
traces. Lip traces hold multifarious features and could be analyzed in different ways to identify the links
with personal identifying features. Machine Learning holds strong application in this domain, especially
for pattern recognition and further interpretation. This work is focused on a brief survey of existing
machine learning approaches in Cheiloscopy. Also, a comparative study of predictive models has been
presented based on an original dataset of lip prints where supervised models have been used to predict
the biological sex of the persons using their lip traces. Machine learning on one hand automatizes
the identification process and poses a significant potential in analyzing huge number of lip traces with
considerable accuracy.
Keywords
Cheiloscopy, Machine Learning, Lip Prints, Biometrics, Image Processing, Pattern Recognition
1. Introduction
The outer surface of the human lips has many ridges and depressions that form a distinct
pattern called "lip impression". The study of lines, fissures, wrinkles and stretch marks on the
lip is called "Cheiloscopy". Although Cheiloscopy is a relatively new field among the large
number of identification tools available to forensic experts, extremely useful information such
as the identity of a person can be obtained from it. This is because they remain relatively stable
and show gender differences [1]. Originally, Cheiloscopy has been a manual procedure where
tools such as magnifying glasses, especial lamps and microscopes were used to analyze lip
prints, making it prone to human errors. Fortunately, computational algorithms could be used
to mitigate those errors. Furthermore, among numerous algorithms capable of solving these
situations, the optimal algorithm needs to be selected, based on the efficiency, lines of code,
memory utilized or a combination of all of them. In addition to this, for many applications
there is not enough knowledge to transform the inputs into the desired outputs pertaining
to forensics. The power of data science stands highly significant in this perspective, where
the insights obtained from data could complement and enrich the traditional knowledge of
forensics, which is essentially the essence of Machine Learning (ML).
ICAIW 2021: Workshops at the Fourth International Conference on Applied Informatics 2021, October 28–30, 2021,
Buenos Aires, Argentina
" agustinsabelli@hotmail.com (A. F. Sabelli); paragc@ieee.org (P. Chatterjee); fpollo@frba.utn.edu.ar
(M. F. Pollo-Cattaneo)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
29
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
ML is a subfield of Artificial Intelligence (AI); AI is defined as the intelligence exhibited by an
artificial entity to solve complex problems. This entity is generally assumed to be a computer or
machine. In other words, AI is the ability of an entity to learn from data and make decisions like
a human being without being explicitly programmed. Unlike humans, machines do not require
breaks, are able to analyze huge amounts of data simultaneously and present a low error rate
[2]. There are many efficient automatic methods in Cheiloscopy using algorithms like dynamic
time warping, top-hat transform, vote counting and the Hough transform [3]. Nonetheless, not
a substantial work has been performed in the recent years to obtain, apart from the identity,
meaningful information of a person such as the sex or age.
This work focuses on the predictive aspect of machine learning algorithms in Cheiloscopy and
compares different supervised learning algorithms that can be used to identify the biological sex
of a person based on their lip traces. First, the basic pipeline of a biometric authentication system
is briefly described. For each step, the state-of-the art is reviewed, analyzed and discussed. Then,
the implementation of each of the stages for this particular use case is explained. Moreover, the
performance is evaluated for each classification algorithm in the results section. Finally, the
conclusions are drawn based on the application and system performance, identifying the future
lines of research as well.
2. Machine Learning in Cheiloscopy
In the field of biometrics, ML stands out for its ability to increase precision in the identification
process. Biometric characteristics taken first instance are not al-ways the same as those taken a
second time. Consequently, the use of machine learning techniques such as artificial neural
networks, fuzzy logic, evolutionary computing, etc., has increased in demand [4].
An automated biometric system aims to correctly predict the identity of a per-son based on a
biometric sample or check if it matches an existing one stored in a data base. This is done in
five steps (see Figure 1).
Figure 1: Basic pipeline of a biometric authentication system
Every step can be seen as a separate ML task. For each task, it is necessary to find the optimal
parameters and settings in order to improve the accuracy of the whole authentication process.
2.1. Input data
The input data refers to the raw data obtained directly from the sensor or data source. For this
case, that would be the lip impressions. These can be collected from people by pressing their
lips, with lipstick previously applied, against a cellophane paper or tape. Alternatively, they can
be obtained from the surface of body parts, fabrics, or objects (see Figure 2) by using a brush
and revealing powder [5].
30
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
Figure 2: Lip impression obtained using conventional revealing powder
Within the scope of this work, the only current open access databases found related to
Cheiloscopy samples are SUT-Lips-DB [6] and the Biometrics Research at University of Silesia1 .
2.2. Segmentation
Segmentation or identification is the process in which the region of interest (ROI) is extracted
from the input data, which can be done by techniques like Object Detection (DO). This computer
vision technique creates a bounding box around each object found (See Figure 3). In Cheiloscopy,
the ROI is the mucosal part called the Klein zone. This zone contains the characteristic patterns
of the lips [7]. Furthermore, another important role that DO plays is to filter and eliminate any
image that does not match a lip impression.
Figure 3: Digitized lip impression and its bounding box after applying DO
Alternatively, Image Segmentation (IS) is another option for segmentation. This technique
creates a pixel mask for each object in the image, giving much more image granularity and
enabling detailed analysis [8, 9] (See Figure 4).
1
Biometrics Research at University of Silesia http://www.biometrics.us.edu.pl
31
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
Figure 4: Digitized lip impression after applying IS
2.3. Pre-processing
Generally, the data obtained from different data sources are not standardized and the pre-
processing aims at solving that. In general, this process is carried out in several steps that
reduce the complexity and increase the accuracy of the classifying algorithms [18]. Because
it is not practical to perform a different pre-processing method for every single image, each
of them is manipulated and trans-formed in such a way that any algorithm can process it.
Table 1
State-of-the-art of pre-processing techniques used in Cheiloscopy
Feature enhancement
Undesirable elements
Grayscale conversion
Background removal
Brightness/contrast
Figure detection
Morphological
Normalization
Binarization
adjustment
operations
filtering
Authors
Smacki et al., [10] ∙ ∙ ∙ ∙ ∙
Wrobel et al., [11] ∙ ∙ ∙ ∙ ∙ ∙
Jain et al., [12] ∙ ∙ ∙ ∙
Wrobel and Froelich [13] ∙ ∙
Mousavi and Zarrabi [14] ∙ ∙ ∙ ∙ ∙
Travieso et al., [15] ∙ ∙ ∙ ∙ ∙ ∙ ∙
Niu et al., [16] ∙ ∙
Lopez-Sanchez et al., [17] ∙
32
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
The pre-processing stage is of great importance in Cheiloscopy since lip impressions usually
have many undesirable elements: fragments of skin, hair, and other undesired elements, which
generate noise. For this reason, images of lip impressions should be cleaned and have any
undesirable elements removed from them [11].
From the literature review of articles published during the last 5 years obtained from relevant
and recognized scientific sources (Scopus and IEEE Xplore), a lack of specific or standardized
way of pre-processing digitalized lip impressions for Cheiloscopy was inferred. In this respect,
Table 1 shows the most common processes.
2.4. Feature Extraction
In the process of feature extraction, an initial set of data is reduced to more manageable groups
for further processing, in this case, leading to dimensionality reduction. Because data often
contains a large number of variables, it requires a lot of computing resources to process them
[19]. In the case of lip impressions, the features of the Klein zone and the labial region are
extracted.
2.4.1. Feature extraction using algorithms
Wrobel et al., [11], analyze the grooves’ bifurcations of the upper and lower lips for their
simplicity and unique pattern. To find such bifurcations, all black pixels in the digitized lip print
are analyzed. The lip impression is then defined by a set of bifurcation systems determined
by the Euclidean distance between their centers and their orientation angle (calculated based
on the three angles between the bifurcations). Smacki et al., [10], obtain the lip pattern using
the Top-Hat transform and filtering with special structures. Jain et al., [12], use Fast-Match,
a Template Matching algorithm, to obtain a matrix of features. Wrobel and Froelich [13] and
Mousavi and Zarrabi [14], extract the segments found in the lips using the Hough transform.
These are later used to find the straight lines that make up the lip patterns. Travieso et al.,
[15], measured the point-to-point height and width of the labial contour divided into four
regions. Lopez-Sanchez et al., [17], implement various dimensionality reduction techniques
(Random projection, Principal Component Analysis, Independent Component Analysis, Simple
Autoencoder) that are combined with different machine learning classifiers. Niu et al., [16],
extract Gabor and Local Binary Pattern (LBP) characteristics from lip prints. Norhikmah et
al., [20], make use of Two-dimensional Principal Component Analysis (2DPCA) as a feature
extraction method. Wrobel et al., [21], mean distances, curvatures, contour shapes and lip area.
These measurements form a vector of characteristics for each individual.
2.4.2. Manual Feature Extraction
Although there are different systems for classifying lip grooves, Kazuo Suzuki and Yasuo
Tsuchihashi designed one that classifies them into six different types (see Figure 5) [22]. This
proposal is superior as it provides greater detail and is easy to understand [23]. It was also
concluded that the morphological pattern of the grooves is unique and exclusive [22].
In the literature, several papers illustrated used a traditional process of collecting lip prints
using cellophane paper, eventually scanning and analyzing them using Adobe Photoshop
33
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
Figure 5: Groove classification proposed by Kazuo Suzuki and Yasuo Tsuchihashi
6.0 [24, 25, 26]. These are subsequently studied and classified according to the Suzuki and
Tsuchihashi classification.
2.5. Classification
Classification is the process by which a set of data goes through a categorization process to
assign different classes (often referred to as a target, label, or categories). This can be done
on structured or unstructured data. Since there are no specific guidelines to figure out which
algorithms to apply to a specific problem, it is recommended to conduct controlled experiments
Table 2
State-of-the-art of ML algorithms used in Cheiloscopy
Multilayer Perceptron
Rule-Based Classifier
Self-Organizing Map
Function Classifiers
Fuzzy clustering
Hidden Markov
Support Vector
ML is not used
Decision Tree
Naive Bayes
Radial Basis
K-Nearest
or similar
Machine
Models
K*
Authors
Smacki et al., [10] ∙
Wrobel et al., [11] ∙ ∙ ∙ ∙ ∙
Jain et al., [12] ∙
Wrobel and Froelich [13] ∙
Mousavi and Zarrabi [14] ∙
Travieso et al., [15] ∙ ∙
Niu et al., [16] ∙
Lopez-Sanchez et al., [17] ∙ ∙ ∙
Norhikmah and Haris[20] ∙
34
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
and find out which algorithm and configuration performs best [27]. This is evident in the
following table (Table 2), where the algorithms used to process the labial images are listed.
3. Materials and Methods — Predictive models in Cheiloscopy
This work was focused to review and compare different supervised learning algorithms in terms
of their performance and effectiveness to identify the biological sex of a person based on their
lip prints. The methodology is presented in five steps, including the entire workflow from
dataset preparation and preprocessing till the modeling and prediction.
3.1. Dataset preparation
Primarily, two common datasets were considered for the study — the Biometrics Research
database from the University of Silesia, and the SUT-Lips database. The first database does not
include the sex of the subjects in the metadata and was thus discarded, and the latter was selected
as the final data source. The SUT-Lips-DB contains 50 folders, each having several lip traces as
JPG files only for one person. However, after rejecting the lip prints with blurry appearance, 43
were selected based on their definition and clarity. Furthermore, for each folder only the best
image was selected, reducing the selection to only one print per person. Subsequently, the final
data set contains lip traces of 26 females and 17 males.
3.2. Data segmentation
Due to high level of noise and undesired elements in the image, most of the lip traces’ ROI
proved to be very hard to extract by automatic means and hence data segmentation was done
on a selective and manual basis. Among the unwanted elements in the images, there were facial
hair, finger prints and oral mucosa prints (see Figure 6).
(a) Finger print (b) Oral mucosa (c) Facial hair
Figure 6: Lips images
Adobe Photoshop was used to extract the ROI out of the lip prints; with the help of the
magnetic loop, a tool used to make automatic selections, it was possible to separate the lower
lip from the upper lip (see Figure 7). The advantage of the magnetic loop is that as points are
selected, it automatically detects and picks the best path based on the contours. This tool has
the peculiarity of adapting to the image that is below the area with which we are working. For
this reason, it is very useful for selecting contours.
35
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
Figure 7: ROI of the lip print
After the segmentation, the traces of the upper and lower lip are saved in Portable Net-
work Graphics (PNG) format in separate files. The naming convention for these files was
-. This is important, because they are treated differently at
the beginning of the next stage depending on whether it is an upper or lower lip.
3.3. Pre-processing
First, based on the naming convention the image is vertically flipped if it is an upper lip. In case
of a lower lip trace, it is not. This is because the algorithm that handles the feature selection
later on was coded taking into consideration a lower lip trace. Nevertheless, the code is robust
enough to deal with this.
The transparent pixels (of alpha channel value zero) are replaced by pixels of white color
(equivalent to [255, 255, 255, 0] in [R, G, B, A] notation). After that, the image is converted to
grayscale and horizontally aligned. To accomplish the latter, the corners of the lip are searched.
Once the corners are found, the anchor point that will be used to perform the rotation which
will horizontally align the lip is calculated. This point is the middle spot between the corners
(see Figure 8).
(a) Corners and anchor points (b) Horizontally aligned lip
Figure 8: Lips corners
The next step is to get rid of the blank space left after the rotation that does not provide
any information. To do this, the minimum bounding rectangle that can enclose the print is
calculated. Taking this rectangle into account, the image is cropped. Afterwards, the image is
resized so that all prints are the same size. This process is known as normalization and in this
case it is 1500x500 pixels. Finally, as we are only interested in the shape, the image is binarized
(see Figure 9).
36
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
(a) White space removed and normalization (b) Binarization
Figure 9: Lips Normalization
3.4. Feature selection
For all images the same biometric features have been determined. These were mainly inspired
by Wrobel et al., work [21]. Each set of lip print-based features are denoted as 𝑓𝑛 = [𝑓1 , . . . , 𝑓𝑛 ]
(see Table 3). For each side of the lip, 40 features have been extracted making 80 in total for
the whole lip trace. These have been exported to a .csv file that is later used to feed the
classification algorithms.
In addition, the features were standardized by removing the mean and scaling to unit variance.
This was done to avoid bad behavior from the ML estimators used in the classification stage.
Table 3
Measurement of the lip print-based features
# Type of measuring Feature Vector Visualization
(𝑎) Perimeter 𝑓1 = [𝑝]
(𝑏) Area 𝑓2 = [𝑎]
(𝑐) Solidity 𝑓3 = [𝑠]
(𝑑) Equivalent diameter 𝑓4 = [𝑒𝑑]
37
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
(𝑒) Extent 𝑓4 = [𝑒𝑥]
Main peaks and valley of
(𝑓 ) 𝑓5 = [𝑝1 , 𝑣1 , 𝑝2 ]
the vertical projection
Maximum length of the
(𝑔) 𝑓6 = [𝑙]
horizontal projection
Distance from the left
𝑓7 = [𝑙𝑑1 , 𝑙𝑑2 ,
(ℎ) corner to the outer edges
𝑙𝑑3 , 𝑙𝑑4 , 𝑙𝑑5 , 𝑙𝑑6 ]
[80∘ , 50∘ , 30∘ , 20∘ , 10∘ , 2∘ ]
Distance from the right
𝑓8 = [𝑟𝑑1 , 𝑟𝑑2 ,
(𝑖) corner to the outer edges
𝑟𝑑3 , 𝑟𝑑4 , 𝑟𝑑5 , 𝑟𝑑6 ]
[80∘ , 50∘ , 30∘ , 20∘ , 10∘ , 2∘ ]
Distance from the upper
𝑓9 = [𝑐𝑑1 , 𝑐𝑑2 ,
center to the outer edges
(𝑗) 𝑐𝑑3 , 𝑐𝑑4 , 𝑐𝑑5 , 𝑐𝑑6 ,
[2∘ , 10∘ , 30∘ , 60∘ , 90∘ ,
𝑐𝑑7 , 𝑐𝑑8 , 𝑐𝑑9 ]
120∘ , 150∘ , 170∘ , 178∘ ]
Distance from the upper
𝑓10 = [𝑖𝑑1 , 𝑖𝑑2 ,
center to the inner edges
(𝑘) 𝑖𝑑3 , 𝑖𝑑4 , 𝑖𝑑5 , 𝑖𝑑6 ,
[2∘ , 10∘ , 30∘ , 60∘ , 90∘
𝑖𝑑7 , 𝑖𝑑8 , 𝑖𝑑9 ]
120∘ , 150∘ , 170∘ , 178∘ ]
Length of the skeletonized
(𝑙) 𝑓11 = [𝑠𝑘]
lip print
3.5. Classification
Since the number of features collected from the previous stage was substantially high, only
the most relevant ones were preserved. The Extra Tree Classifier was used to perform this
dimensionality reduction. Also known as Extremely Randomized Trees, this is a type of ensemble
learning technique composed of a large number of decision trees where the final decision is
obtained taking into account the prediction of every tree. For this type of classifier, all the
38
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
features and splits (questions about the data) are selected at random. In this forest structure,
the relevance of a feature is given by the Gini importance. To achieve the transformation of
data from a high-dimensional space into a low-dimensional space, each feature is ordered in
descending order according to this value. Finally, only the top k features are selected.
As a result, only a certain number of features (Table 4) out of the originally 80 features were
kept. The final number of features depends on the best possible result for each classification
algorithm. Furthermore, with the intention of getting better and more stable results the dataset
was split into different sequences of train and test portions (also known as cross-validation)
[28, 29]. Hence, it was split into 5 different training and validation datasets. Since the k-fold
approach was used [30], and k is equal to 5, the train to test ratio was 34:9 (80% for training an
20% for test) for each fold.
As stated before, the number of samples in the cleaned dataset was pretty small (43 in total).
In addition to this, since the data has been labeled and the goal is to predict or classify future
observations, supervised learning was chosen. In particular, the following algorithms were
selected:
3.5.1. Logistic Regression (LR)
LR is a statistical learning method for classification. The term “logistic” refers to the “log odds”
probability that is modeled. The term “odds” is defined as the ratio of the probability that an
event occurs to the probability that it doesn’t. It seeks to predict the effect of a series of variables
on a binary response variable and classify observations by estimating the probability that an
observation is in a particular category [31, 32].
3.5.2. Multilayer Perceptron (MLP)
MLP is a robust and nonlinear neural network model that operates as an approximation func-
tion. It is the expansion of a simple neural network, with multiple hidden layers that allow
solving extremely complex problems. MLP uses back-propagation as its learning algorithm,
a generalization of the Least Mean Squared rule. One of the major problems of this model is
over-fitting, mainly due to large number of hidden layers being used [33].
3.5.3. Support Vector Machine (SVM)
SVM works under the principle of margin calculation. It can be simply defined as a prediction
tool that looks for a particular division line called a hyperplane that easily separates datasets
or classes, thus avoiding overtraining of the data. This hyperplane is generated in an iterative
manner, which is used to minimize the error. In practice, SVM uses a technique called the
“kernel trick” where a low-dimensional input space is transformed into a higher dimensional
space. This is because a higher dimensional transformation can allow us to separate data in
order to make classification predictions [34].
39
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
3.5.4. Naïve Bayes (NB)
NB is typically used for classification and clustering purposes. It is called naïve (naive), since
it assumes that all variables contribute to the classification and are mutually co-related. This
technique is based on Bayes theorem and is used when the dimensionality of the inputs is high.
Using Bayes theorem, we can find the probability of A happening, given that B has occurred.
NB algorithms are fast and fairly easy to implement. However, their biggest disadvantage is
the need of independent predictors. In most cases, these predictors are dependent and the
performance suffers from it [35].
3.5.5. K-Nearest Neighbor (K-NN)
K-NN is an algorithm where given “n” training vectors, identifies the “k” nearest neighbors of
“c” (a feature vector that we want to estimate its’ class), regardless of labels. It then predicts the
class of “c” based on the neighbors who are majority. The main advantages of this algorithm
are its simplicity and straight forward implementation. On the other hand, the main draw-back
is the complexity in searching the nearest neighbors for each sample [31, 36].
4. Results
Performance statistics for the different classification algorithms are shown in Table 4. Only the
top combinations of features and classification algorithms are displayed.
Table 4
Performance statistics
Classifier # Features Accuracy f1 score AUC
K-NN 28 0.82 0.86 0.80
LR 23 0.79 0.80 0.75
NB 24 0.77 0.81 0.77
SVM 25 0.70 0.77 0.78
MLP 25 0.65 0.66 0.59
The optimal number of features differs across classifiers, but overall it’s less than a half of
the original 80 obtained from the feature selection stage. The accuracy of a ML classification
algorithm is one way to measure how often the algorithm classifies a data point correctly. The f1
score and the Area Under the Curve (AUC) values are also of utter importance. This is because
the first one indicates the precision and recall of the model, whereas the second represents how
much the model is capable of distinguishing between classes (male or female) [35]. In general,
the Accuracy and AUC is above 75% for all the models, while the f1 score is greater than 80%.
The best accuracy for the sex prediction was verified in the K-NN model (0.82). The top f1
score (0.86) and AUC (0.80) can also be found in this particular model. Contrastingly, the lowest
accuracies are detected from MLP (0.65). The same is the case for the f1 score (0.66) and AUC
(0.59).
40
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
5. Conclusions
This paper focused on the use of different supervised learning algorithms to identify the
biological sex of a person based on their lip traces. We also presented a summary of the state-of-
the-art in Cheiloscopy in the context of ML. An original dataset was created out of the features
automatically extracted from one of the only two open-source databases available for lip prints.
This dataset was later used to use in the classification algorithms and their performance was
compared and analyzed in the previous section. Comparing the different types of models used,
K-NN provided the best performance, with all the models providing satisfactory accuracy in
determining the biological sex of a person based on their lip traces, thus validating the focal
objective of this work. The principal challenge was in the aspect of image segmentation, which
was done manually, considering the high amount of noise and number of undesired elements.
Scarcity of open access databases containing images of lip prints posed another challenge,
especially with respect to the training of algorithms. Also, fine tuning the hyper-parameters of
the estimators for each classification model was a significant challenge; however, it was resolved
using a hyperparameter tuning function [37]. In the future, further work could be performed in
the domain of unsupervised algorithms and deep learning as well, provided the availability of
sufficiently big datasets. Similarly, it might provide further information like age that could still
be extracted out of lip prints.
References
[1] A. Kumar, S. N. Prasad, V. Kamal, S. Priya, M. Kumar, A. Kumar, Importance of cheiloscopy,
Int J Oral Care Res 4 (2016) 48–52.
[2] L. Rouhiainen, Artificial Intelligence: 101 things you must know today about our future,
Lasse Rouhiainen, 2018.
[3] S. Sandhya, R. Fernandes, Lip print: an emerging biometrics technology-a review, in: 2017
IEEE International Conference on Computational Intelligence and Computing Research
(ICCIC), IEEE, 2017, pp. 1–5.
[4] P. Akulwar, N. A. Vijapur, Secured multi modal biometric system: A review, in: 2019 Third
International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC),
IEEE, 2019, pp. 396–403.
[5] S. Kannan, K. Muthu, S. Muthusamy, P. Sidhu, Cheiloscopy-a vital tool in crime investiga-
tion, Int J Forensic Sci Pathol 3 (2015) 89–93.
[6] D. Mrozek, Sut-lips-db - a database of lips traces, 2017. URL: https://dx.doi.org/10.21227/
H2R04P. doi:10.21227/H2R04P.
[7] L. Chatra, T. Peter, A. Ahsan, et al., Cheiloscopy, International Journal of Forensic
Odontology 1 (2016) 48.
[8] F. Velosa, H. Florez, Edge solution with machine learning and open data to interpret signs
for people with visual disability., in: CEUR Workshops Proceedings, 2020, pp. 15–26.
[9] J. Yepes Zuluaga, F. Yepes-Calderon, Tensor domain averaging in diffusion imaging of
small animals to generate reliable tractography, ParadigmPlus 2 (2021) 1–19.
[10] L. Smacki, J. Luczak, Z. Wrobel, Lip print pattern extraction using top-hat transform,
41
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
in: Proceedings of the 9th International Conference on Computer Recognition Systems
CORES 2015, Springer, 2016, pp. 337–346.
[11] K. Wrobel, P. Porwik, R. Doroz, Effective lip prints preprocessing and matching methods,
in: Proceedings of the 9th International Conference on Computer Recognition Systems
CORES 2015, Springer, 2016, pp. 347–357.
[12] S. Jain, V. Poojitha, M. Bhatia, A cheiloscopic approach for unique identification among
indian subpopulation, in: Intelligent Communication, Control and Devices, Springer, 2018,
pp. 1729–1738.
[13] K. Wrobel, W. Froelich, Recognition of lip prints using fuzzy c-means clustering, Journal
of Medical Informatics & Technologies 24 (2015).
[14] A. S. Mousavi, H. Zarrabi, A hidden markov model (hmm) scheme for lip based identifica-
tion utilizing vertical grooves angles, International Journal of Computer Applications 975
(2018) 8887.
[15] C. M. Travieso, A. G. Ravelo-García, J. B. Alonso, J. M. Canino-Rodríguez, M. K. Dutta,
Improving the performance of the lip identification through the use of shape correction,
Applied Intelligence 49 (2019) 1823–1840.
[16] B. Niu, J. Sun, Y. Ding, Lip print recognition using gabor and lbp features, DEStech
Transactions on Computer Science and Engineering (2016).
[17] D. López-Sánchez, A. G. Arrieta, J. M. Corchado, Machine learning methods for automatic
cheiloscopy on facial images, in: 8th International Conference on Imaging for Crime
Detection and Prevention (ICDP 2017), IET, 2017, pp. 55–60.
[18] C. Zhu, D. Gao, Influence of data preprocessing, Journal of Computing Science and
Engineering 10 (2016) 51–57.
[19] D. Sanyal, N. Bosch, L. Paquette, Feature selection metrics: Similarities, differences, and
characteristics of the selected models., International Educational Data Mining Society
(2020).
[20] M. K. Norhikmah, S. K. H. Angriawan, Implementation of 2dpca and som algorithms to
determine sex according to lip shapes, in: 2019 4th International Conference on Information
Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, 2019, pp.
101–106.
[21] K. Wrobel, R. Doroz, P. Porwik, J. Naruniec, M. Kowalski, Using a probabilistic neu-
ral network for lip-based biometric verification, Engineering Applications of Artificial
Intelligence 64 (2017) 112–127.
[22] K. Suzuki, Y. Tsuchihashi, Personal identification by means of lip prints, J Forensic Med
17 (1970) 52–57.
[23] M. Bajpai, N. Pardhe, B. Chandolia, M. Arora, Cheiloscopy-an overview of its limitations
and future per-spectives, J For Med Leg Aff 1 (2016) 106.
[24] S. Nadeem, G. Manzoor, S. Pervez, Lip forensics; cheiloscopic pattern among population
of punjab, pakistan., The Professional Medical Journal 26 (2019) 1177–1182.
[25] P. Kesarwani, A. Choudhary, Establishing role of cheiloscopy in person identification and
sex determination, Journal of Orofacial Research (2017) 4–7.
[26] B. S. Sharma, V. Gupta, H. Vij, E. Sharma, N. Tyagi, S. Singh, et al., Cheiloscopy: A tool for
antemortem identification, Indian Journal of Dental Sciences 9 (2017) 176.
[27] J. Brownlee, Machine learning mastery with weka, Ebook. Edition 1 (2019) 4.
42
Agustín F. Sabelli et al. CEUR Workshop Proceedings 29–43
[28] D. Berrar, Cross-validation., 2019.
[29] J. Hernandez, K. Daza, H. Florez, Alpha-beta vs scout algorithms for the othello game, in:
CEUR Workshops Proceedings, volume 2846, 2019, pp. 65–79.
[30] S. L. Developers, Cross-validation: Evaluating estimator performance, 2018.
[31] S. Ray, A quick review of machine learning algorithms, in: 2019 International conference
on machine learning, big data, cloud and parallel computing (COMITCon), IEEE, 2019, pp.
35–39.
[32] H. Florez, S. Singh, Online dashboard and data analysis approach for assessing covid-19
case and death data, F1000Research 9 (2020).
[33] H. Taud, J. Mas, Multilayer perceptron (mlp), in: Geomatic Approaches for Modeling Land
Change Scenarios, Springer, 2018, pp. 451–455.
[34] M. Somvanshi, P. Chavan, S. Tambade, S. Shinde, A review of machine learning techniques
using decision tree and support vector machine, in: 2016 international conference on
computing communication control and automation (ICCUBEA), IEEE, 2016, pp. 1–7.
[35] B. Mahesh, Machine learning algorithms-a review, International Journal of Science and
Research (IJSR).[Internet] 9 (2020) 381–386.
[36] J. Hernandez, H. Florez, An experimental comparison of algorithms for nodes clustering in
a neural network of caenorhabditis elegans, in: International Conference on Computational
Science and Its Applications, Springer, 2021, pp. 327–339.
[37] Scikit-learn developers, Tuning the hyper-parameters of an estimator, 2007.
43