Anisha Kundu (Author) & Akshat Gupta (Co-Author) In recent times, machine learning has become one of the key aspects of data handling. After years of research by the scientists, neuroscientists, and psychologists, numerous feasible technologies are available; some credit may go to the commercial and law enforcement applications as well. This paper proposes a technique for biometric recognition, which analyzes the geometry of the hand to find and isolate the vein patterns from near-infrared palm and wrist images and extract features based on repeated line tracking algorithm and maximum curvature algorithm.

1. Integration of Machine Learning in Attendance and Payroll
Anisha Kundu1
Akshat Gupta2
ABSTRACT
In recent times, machine learning has become one of the key aspects of data handling. After
years of research by the scientists, neuroscientists and psychologists, numerous feasible
technologies are available; some credit may go to the commercial and law enforcement
applications as well. This paper proposes a technique for biometric recognition, which
analyze the geometry of the hand to find and isolate the vein patterns from near infrared palm
and wrist images; and extract features based on repeated line tracking algorithm and
maximum curvature algorithm. In plain words, it is a computer application for automatically
identifying a person from its palm and wrist vein images. This technique could be used to
create an automated attendance management system, which implicitly detects the employee
when he/she enters the office gate and marks their attendance by recognizing them. The line
tracking algorithm tracks the dark line pattern at random points and repeats the process until
all the pixel points are collected. Whereas, the maximum curvature algorithm identifies the
curved like structure at the centre of the vein to draw the pattern using the position of the
structure. The extracted patterns are then matched using robust template matching technique,
where pixel-to-pixel comparison is made. When compared to traditional attendance marking
this system saves the time and also helps to monitor the employees.
Once the attendance of the employees is collected, this data can be used to analyse other
aspects such as: measuring the employee engagement, studying the workforce patterns of a
particular department or region, analysing employee churn and turnover. All these come
under the evolving application field of analytics for Human Resource Management, called,
the Human Resource Predictive Analytics. It helps in optimizing the performances and
produce better return on investment for an organization using the concept of decision making
based on data collection and predictive models, for effective and efficient management of
human resources. Expert system or knowledge-based systems, which were earlier used for
such decision making, demonstrated few limitations. Hence in this paper, we will propose
Intelligent Human Resource Information System (i-HRIS) which applies Intelligent Decision
Support System (IDSS) and Knowledge Discovery in Database (KDD) to improve the
structured, semi-structured and unstructured decision-making process. With a set of Artificial
Intelligent tools such as knowledge-based reasoning and machine learning, the Intelligent
Decision Support System stores and processes information. The concept of Machine Learning
is used to discover useful information from past data and experience to support decision
making process by applying hybrid intelligent techniques.
1. Introduction
With the advancement of machine learning, Human Resource Management System is now
able to eliminate repetitive tasks, reduce employee attrition and improve employee
engagement. By using various algorithms, we are able to simulate the behavior of human

2. and to re-imagine the experience of the employees. Artificial Intelligence helps in drawing
out the insights and inferences, which might remain uncovered at all from general
manpower. It has bought this good news for Human Resources and has given it a chance to
catch up with the digital transformation. In summation, the proposed framework consists
of input subsystems, decision making subsystems and output subsystems; for a better
management of human resources.
It has been quite a decade that the idea of computers learning autonomously has been
around. So why machine learning has gained so much ground and what has changed in
recent years? Various possible reasons could be:
Increased computing power; improved performance, driven by gaming, graphic processing
units (GPUs), at the level of parallel computation of simple operations, commonly used by
deep learning algorithms. In-memory databases, together with the wide adoption of multi-
core architectures, has paved the way for extremely efficient implementations of machine
learning algorithms.
Big data, being another reason. Huge data sets provided by various sources, are the basis
for training machines. For an example, the ability to tag individual faces on pictures (with
names) on social media has led to the largest database of faces in the world. These social
media, such as Instagram, Facebook etc. can train machines to learn in terms of visual
recognition. (artificial-intelligence-in-hr-and-payroll-embracing-disruption, n.d.)
On the basis of machine learning algorithms, devices can be trained to predict and interpret
sophisticated situations in the future.
Due to the vast number of free, high-quality, open-source software packages making
machine learning accessible to a large audience of data scientists and developers, machine
learning is becoming easier to apply.
Machines can recognize objects, read and understand text, write, listen and talk. This shows
how machine learning can bring intelligence to business environments.
One of the most important, yet one of the most cumbersome and time-consuming daily
activities, of any company is the Payroll administration. Keeping this process to bare
minimum in time as well as cost, means savings and enhanced efficiency to the business,
as it does not generate direct revenues. Hence why not create a software to take care of this
process, so that you can focus on important daily core tasks.
The Payroll system manages wage calculation, allowances, absences, expenses, benefits,
tax deductions etc. with very little input from you, such as wage details and work hours of
an employee. A payroll automation system consolidates employee data and regulatory
rules, hence avoid inaccurate financial statements and penalties. (4-trends-in-payroll-
management, n.d.). It is estimated that automated payroll system helps reduce costs on
paycheck errors by around 80%.
Other advantages of having an automated payroll system are:

3. i. To create customized reports, where you can define rows and columns, fields and
other rules; and generate recurring reports scheduled with smart alerts.
ii. To add security, without relying on a third party and exposing sensitive employee
data. Automated payroll system nowadays can be easily managed internally, by the
authorized staff.
iii. To create a smarter rostering process. Through machine learning, rostering will be
able to take into account certain external inputs like weather, events or concerts,
public holidays and other seasonal variations and then predict the staffing
requirements.
iv. To have auto-approval of leaves, reimbursements etc. through the use of machine
learning. With only exceptions being surfaced to managers for approval, spending
countless hours on unnecessary manual approvals could be eliminated.
v. To have a continuous compliance over any potential legislative changes. Payroll
systems will be able to track legislative changes, using machine learning and
artificial intelligence; and analyze the impact of those changes on a business’
payroll. It enables business owners to be on top of payroll legislation by notifying
them of potential issues.
We chose a technique for biometric recognition, which analyze the geometry of the hand
to find and isolate the vein patterns from near infrared palm and wrist images; and match
them with the registered vein pattern images, say from our employee database, to identify
individuals, genuinely.
Since no physical contact is needed to obtain the palm or wrist vein image, so it is better
than fingerprint and iris scanning, and will not cause any displeasure to the subject. It is
hard to forge vein patterns, unlike fingerprints which nowadays can be easily captured and
reproduced by any quick drying adhesives. Vein patters are unique as different people have
different vein patterns and it undergoes less change following the growth of age. Unlike
fingerprint or facial acquirement, the states of skin, temperature and humidity have little
effect on the vein image.
Under skin patterns are captured by a special camera, with lighting of near-infrared lights.
Infrared lights are absorbed by haemoglobin, resulting in dark shadow pattern (Kono,
“Near-infrared finger vein patterns for personal identification) (Lin C.-L. a.-C., 2004)
(Kono, A new method for the identification of individuals by using of vein pattern matching
of a finger, 2000) of the veins or blood vessels. Some irregular shading and noise might
also generate, along the vein patterns, caused by the varying thickness of muscles and
bones, and avoidance of continuity. Therefore, the captured images need to be processed
to eliminate those irregularities and obtain a clear view of veins. While executing complex
differential operations and optimization of the line extraction, the computational costs gets
exponentially high. Even the processing time of an image in such a case may get prolonged.
In Miura et. al (N Miura, 2004) by using the repeated line tracking algorithm, to extract
vein patterns of an unclear image can be processed, but since the number of times the
tracking point moves on thin veins tends to be statistically very small, it may not be

4. adequate to extract thin veins. Hence Miura et. al (Miura, 2007) proposes a method where
the curvature of the image profiles are checked and only the centrelines of veins are
emphasized. The positions which gives the local maximal of the curvatures of a cross-
sectional profile of a vein image, helps detect the centrelines of veins, and a robust method
is used to detect the maximum curvature against temporal fluctuations in the width and
brightness of vein.
After enhancing the image using various pre-processing tools such as segmentation,
normalization, edge detection etc., we need to extract the features of the image using feature
extraction algorithms, none other than “repeated line tracking algorithm” and “maximum
curvature points”. Finally, we match the images based on a robust template- matching
technique.
Once image is captured, processed and stored, we apply the HRIS (Human Resource
Information System), KDD (Knowledge Discovery in Database) and IDSS (Intelligence
Decision Support System). When an automated system was developed and employee data
were used for the first time in the late 1960s (M., 2014), the concept of HRIS came into
play. KDD is a widely used term in intelligent data processing. Fayyad (U., 1997) defines
KDD as a nontrivial process of identifying potentially useful, valid, novel, and ultimately
logical patterns in a data set. IDSS is a new type of DSS that is integrated with AI
techniques. This system is a combination of basic function models of DSS and knowledge
reasoning techniques of AI. It solves complex, imprecise and ill-structured problems
(Ribeiro R., 2006).
2. Literature Review
Due to the convenience of image acquisition, several vein features in the hand have been
well studied, such as finger veins, hand veins and hand-dorsal veins. In particular, the palm
veins have gained more attention from researchers due to their more abundant texture
information and easy acquisition. Recently, studies of palm veins have focused on feature
extraction methods that acquire the salient features more efficiently. (Chen, 2007) (Zeman,
2004)
Materials and Conditions
A CIE vein database containing 1200 infrared palm images and 1200 infrared wrist images,
each of 1280×960 resolution and of a 24-bit bitmap. The images are named in the form
representing the person number, palm or wrist, left or right, series number followed by the
picture number. The images are already pre-processed, with a proper lighting and contrast,
well maintained distance from the camera and sample, less distorted. Each person folder
has sub- folders for left and right hand, which further contains sub-folders for each series.
Images have the information in the names, for example: in Figure 2.1, it says
“P_o001_L_S1_Nr1” is the 1st image of the 1st series of Left palm of 1st person.

5. Fig. 2.1: Image name: P_o001_L_S2_Nr3 Fig. 2.2: Image name: P_o001_R_S2_Nr3
Basic Concepts
a. Image reading:
The process of selecting, storing, displaying the image and reading the input image for
processing. A 1280x960 resolution vein image, Image1(x, y), is selected from the CIE vein
database.
b. Pre- processing:
It takes both input and output as intensity images. Helps in eliminating the unnecessary
distortion and also improving some important features of images, which may be required
in future for further processing.
Fig 2.3: Brief view of image segmentation
2. Pre Processing
Complimentary
Image
Thinning
Connecting broken lines
Local Thresholding
1.Image Reading
Global
Thresholding
Edge
Detection
Cropping
Image
Selecting ROI
3. Image
Normalization
4. Post Processing
Smoothing
image
Erosion
Dilation
5. Binary Image

6. c. Global thresholding:
Here the pixels of the image are dominated into two mode, background and foreground. A
threshold value helps in separating the pixels, acts as a decision factor. If the intensity point
is greater than threshold, then it is accepted as the foreground object point else if lesser than
equals to the threshold, is the background point. It is implemented on the image as the
brightness of the image is more than that of the background.
𝐼𝑚𝑎𝑔𝑒2(𝑥, 𝑦) = {
0, 𝑖𝑓 𝐼𝑚𝑎𝑔𝑒1(𝑥, 𝑦) < 𝑡
1, 𝑖𝑓 𝐼𝑚𝑎𝑔𝑒1(𝑥, 𝑦) ≥ 𝑡
d. Edge detection:
In this approach segmentation of images are done based on intensity change, using first or
second order derivatives. It is used to find out the boundary of the object. (Image3(x, y)).
The gradient is denoted as,
∇𝐼𝑚𝑎𝑔𝑒1 ≅ 𝑔𝑟𝑎𝑑(𝐼𝑚𝑎𝑔𝑒1) ≅ [
𝐼𝑚𝑎𝑔𝑒2 𝑥
𝐼𝑚𝑎𝑔𝑒2 𝑦
] = [
𝛿 𝐼𝑚𝑎𝑔𝑒1
𝛿 𝑥
𝛿 𝐼𝑚𝑎𝑔𝑒1
𝛿 𝑥
]
The magnitude M(x,y) of vector ∇𝐼𝑚𝑎𝑔𝑒1 ≅ 𝑔𝑟𝑎𝑑(𝐼𝑚𝑎𝑔𝑒 1) is defined as,
𝑀(𝑥, 𝑦) = 𝑚𝑎𝑔(∇𝐼𝑚𝑎𝑔𝑒1) = √𝐼𝑚𝑎𝑔𝑒22 𝑥 + 𝐼𝑚𝑎𝑔𝑒22 𝑦
The gradient image is denoted as,
∝ (𝑥, 𝑦) = tan−1
[
𝐼𝑚𝑎𝑔𝑒2
𝑦
𝐼𝑚𝑎𝑔𝑒2 𝑥
]
e. Sobel edge detection:
It is edge detection technique that uses the following filter masks,
[
1 0 −1
2 0 −2
1 0 −1
] and [
1 2 1
0 0 0
−1 −2 −1
]
f. Region of Interest (ROI):
It is a required focused region or boundaries of an object over a sample space of dataset
that fulfills the aim of an experiment. (Image4(x, y))
g. Smoothing of image:
Using low pass filters, noise is reduced by smoothing the image. Some of the low pass
filters are based on average values (mean filters) and others median values (median filters).

7. h. Gaussian low- pass filter:
Gaussian low pass filter or Gaussian blur is a smoothing technique that gives Gaussian
function as impulse response. (Image5(x, y)). In 2D the equation:
𝐺𝑙𝑝𝑓(𝑥, 𝑦) =
1
2𝜋𝛾2
𝑒
−(
𝑥2+𝑦2
2𝜎2 )
Where, σ is the standard deviation
i. Normalization of image:
Normalization of image is a technique, which uses histogram stretching or contrast
stretching, where the range of pixel intensity value changes.
Image5 (x,y):
𝐼𝑚𝑎𝑔𝑒5(𝑥, 𝑦) = 𝑀 𝑛 + √(𝐼𝑚𝑎𝑔𝑒5(𝑥, 𝑦) − 𝑀)2 ∗
𝑉𝑛
𝑉
Else:
𝐼𝑚𝑎𝑔𝑒6(𝑥, 𝑦) = 𝑀 𝑛 + √(𝐼𝑚𝑎𝑔𝑒5(𝑥, 𝑦) − 𝑀)2 ∗
𝑉𝑛
𝑉
Where, M is the image mean, V is the image variance Mn is set to 100 and Vn is set 225.
j. Morphological operations:
A technique that analyses and processes on geometrical structures, called “structuring
element”. This small template is positioned and compared at every possible location in
image with the corresponding neighboring pixels based on the operations “hit” or “fit”.
k. Erosion:
The minute details are removed by “erosion” from the image, with reducing the size of
ROI. The boundaries can be determined by subtracting the eroded image from the original
one.
l. Dilation:
“Dilation” has the complimentary effect of erosion. It adds up a layer in between the inner
and outer layer of pixels. Hence, it helps in filling up the void or holes.
m. Local thresholding:
This is corresponding to an image, where the pixels are dominated into two mode,
background and foreground. A threshold value that helps in separating the pixels acts as a

8. decision factor. If the intensity point lesser than threshold, accepted as the object point else
if greater than equals to the threshold is the background point.
𝐼𝑚𝑎𝑔𝑒9(𝑥, 𝑦) = {
0, 𝑖𝑓 𝐼𝑚𝑎𝑔𝑒10(𝑥, 𝑦) ≥ 𝑡
1, 𝑖𝑓 𝐼𝑚𝑎𝑔𝑒10(𝑥, 𝑦) < 𝑡
n. Hit- or miss- transform:
To obtain how the object are related to their surrounding in a binary image, the hit- or miss-
transform is used. It requires two identical or similar structuring elements that analyses the
inner and outer objects in the image.
Resulting images in all the stages of image segmentation are sequentially mapped into the
following:
a. Figure 2.4 original image: Image1(x, y),
b. Figure 2.5 global thresholding: Image2(x, y),
c. Figure 2.6 edge detection: Image3(x, y),
d. Figure 2.7 selection of ROI: Image4(x,y),
e. Figure 2.8 smoothening of the image using Gaussian low pass filter: Image5(x,y),
f. Figure 2.9 normalization of the image: Image6(x,y),
g. Figure 2.10 morphological dilation: Image7(x,y),
h. Figure 2.11 local thresholding image: Image8(x,y),
i. Figure 2.12 thinning: Image9(x,y),
j. Figure 2.13 connecting broken lines using dilation: Image10(x,y),
k. Figure 2.14 complimentary binary image: Image11(x,y).
Fig. 2.4: Original Image Fig. 2.5: Global Thresholding

9. Fig. 2.6: Edge Detection Fig. 2.7: Selection of ROI
Fig. 2.8: Smoothening using Gaussian low pass filter Fig. 2.9: Normalization
Fig. 2.10: Morphological Dilation Fig. 2.11: Local Thresholding
Fig. 2.12: Thinning Fig. 2.13: Connecting broken lines

10. Fig. 2.14: Complimentary Binary Image
Once the image is extracted, it is matched with the images present in the registered
database, to get the final output.
Fig 2.15: Personal identification methods, using vein patterns from palm and wrist.
In HRM, there are many occurrences where HR decision depends on a variety of factors,
such as knowledge, human experience and judgment. These factors can be cause of
inaccurate, inconsistent, unfair and unanticipated decisions. For this reason, data mining
techniques may be used for its reasoning characteristics. Expert system incorporates human
expert knowledge in its knowledge-based component. It is able to mimic the decision
ability of human expert to help them with their routine tasks, even their absence. Figure
Image Reading Pre-Processing Normalization
Post-Processing
Matching
Registration
Vein Extraction
Binary Image
Output
DB

11. 2.16 shows that an IDSS incorporates many AI techniques in its working steps on the basis
of problem nature.
Fig. 2.16: Working steps of IDSS
3. Research Methodology
Repeated Line Tracking Algorithm with Template Matching
Feature Extraction Algorithm consists of the following 5 steps:
a) Determine the start point and the moving-direction attributes for line tracking.
b) Determine the motion of the tracking point and the direction of the dark line.
c) Update the locus space as many time as the points been tracked.
d) Repeat Step a to Step c, N times.
e) Obtain the vein pattern from the locus space.
INTELLIGENT
DECISION SUPPORT
SYSTEM
SITUATION
ASSESSMENT
KDD
New knowledge
pattern
DECISION
MODELING
Automatic Neural
Network, Fuzzy
Logic, Expert
System
Suggested Decision
EXPECTANCY
FORECASTING
Automatic Neural
Network
Forecasted data

12. Matching: For matching technique, the vein pattern data is converted into matching data
and are compared with the registered data. The two widely used line shaped pattern
matching techniques are: Structural matching and Template matching are widely used line
shaped pattern matching. In case of structural matching we use: line ending and bifurcation.
When there are less feature points then we use template matching, which is based on
comparison of pixel values, suitable to segment vein images.
Maximum Curvature Points with Template Matching
Feature Extraction Algorithm includes 3 steps:
a. Extract the Veins’ center positions.
b. Connect the obtained center positions.
c. Label the image.
Matching: Similarly, the obtained pattern is converted into matching data and then matched
with the registered data. To provide any HR related report and to suggest solutions of
structured, semi-structured and unstructured HR problems and making it available to users,
the intelligence based HRIS model consists of three segments: input subsystem, decision
making subsystem, and output subsystems. Figure 3 illustrates the proposed i-HRIS model
for HR functionalities. The description of the suggested model is presented below:
0
Fig. 3: i-HRIS model for HR functionalities
INPUT SUB-SYSTEM
EXTERNAL SOURCES
EXTERNAL SOURCES
HRIS
INTERNAL SOURCES
TRANSCATION PROCESSING
SYSTEM
DATABASE
MANAGEMENT
SYSTEM
PERSONAL
DATABASE
PAYROLL DB
PERFORMANCE
EVALUATION DB
KNOWLEDGE
MANAGEMENT
SYSTEM
DATA MART Data
Warehouse
DECISION PROCESSING SUB-
SYSTEM
OUTPUT SUB-SYSTEM
Strategic
HR
Planning
Module
Payroll Module
Performance
Evaluation
Module
Compensations
and Benefit
Module
Employee
Service Module

13. i. Input Subsystems:
HRIS input subsystems consist of Transaction Processing Subsystem (TPS) and HR
Intelligence Subsystem. The input subsystems section takes HR related data into
operational database that transform input data into the mandatory format for storage. This
part also includes software or other external databases that transform. So once an employee
punched into the organization, TPS gathers it as data into database, and transform it into
useful information such as the employee’s salary, post titles, employee work history etc.
HR intelligent subsystem acts an interface and obtains intelligent data from commercial
databases, related to stakeholders, financial institutions, labour union etc., for a smooth
generation of monthly payment.
ii. Decision Making Subsystems:
For decision making process, a KDD can help to extract knowledge from old data and
decisions by different instruments and techniques including Big-Data Mining, OLAP, and
AI techniques.
iii. Output Subsystems:
The output subsystem consists a variety of models that could provide reports or solutions
or flexible suggestions for HR problems and help solving complex, imprecise and ill-
structured problems by decision making subsystem.
Performance Evaluation Module: This module is very complex, with many rules for each
criterion with different priority, hence fuzzy rule base decision making approach is
considered, which offers a total performance index, considering all criteria, for an
employee.
Compensations and Benefits Module: In this module, HR IDSS may use ANN, for
statistical and financial models to estimate the amount of compensation and benefits
(insurance, pension, profit-sharing, and stock option, and other benefits).
Payroll Interface Module: This module will be developed using financial or accounting
models for timely and accurate structured decision of payments and may incorporate the
information necessary to calculate attendance, any leaves of absences (paid or unpaid),
vacation time, and any other events that interrupted service. It will include information on
salary, wages, and benefits.
4. Analysis and discussion on future trends
With the evolution of technology world, there are more trends and demand in the field of
identity management. The need for a more accurate and secure way of identifying an
individual, gives rise to all these trends and demands and to gain competitive advantage,
one must adopt to these. Few of the future trends in payroll technology, are discussed
below.

14. Mobile Biometric Technology
Biometric human identification can’t always be performed in a controlled office
environment. It might be required, at times, to go in public venues. To speed up the
identification process effectively and efficiently under these situations, mobile biometrics
can be used. The biometric functionality can be achieved on a mobile device either through
its built-in biometric sensors or by attaching portable biometric hardware to it via a USB
cable or through a Wi-Fi connection.
Cloud Based Biometric Solutions
This trend is mainly driven by mobile biometric technology. Instead of saving the biometric
data locally, sending it to the cloud is a safer solution, plus, pairing the mobile biometric
device with a cloud based biometric solution can speed up the identification process even
more.
Biometric Single Sign on (SSO)
As many companies are adopting biometric single sign on over traditional passwords to
secure their networks from data breaches and to minimize password management costs,
this stands as the most popular debates in the current scenario, whether biometrics will
replace passwords. Passwords are vulnerable because: they can be guessed, forgotten,
shared or swapped. On the other hand, biometrics are unique, hard to spoof, and one cannot
lose or share them.
In cases where employees in an organization must log into multiple databases and have
different passwords for each of them which needs to be reset periodically, it can be very
frustrating and may lead to decrease in productivity as well. Hence, with the
implementation of a complete biometric single sign on, the employees no longer have to
remember passwords without threatening the security of the network. (Recent trends in
biometric technology, n.d.)
5. Conclusion
To overcome translation, rotation and scale variance in contact-free palm-vein images, we
propose a robust palm-vein recognition approach. We use the entire palm region for vein
recognition, which not only gains more vein information and reduces complexity but also
decreases restriction of hand posture during registration and authentication. Hence, aids in
genuine registration of attendance of the person. Finally, the obtained biometric data is
stored in the database, processed to fetch the details of any particular employee and
analyzed to track the work history, compensation allowance, performance bonus etc. to
calculate the salary of the employee.

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