A system that is used for time-clocking, creating an all-inclusive electronic record of the process involved in how employees logs in and out of work on working days are referred to as a clocking system. The system has an additional feature of calculating an accurate payroll system, which in turn, can lead to a precise amount the company spent on labour. In essence, an employee clocking system is a process of monitoring the attendance, presence and truancy of employees in a work environment. In this project, the University of Energy and Natural Resources was used as a case study. The existing method of recording the presence of staff to work is by a manual process where employees record their attendance on a paper. The challenge of the existing employee attendance system is the difficulty in tracing old records, safekeeping, lack of confidentiality and the chances of other employees logging in for their truant colleagues. This paper sought to introduce a biometric employee clocking system to help overcome the high level of truancy in workplaces. The results of the experiment we conducted indicate a high accuracy in our system with TAR value of 99.7%. This accuracy rate is much better than the results other researchers obtained. The good accuracy implies that employees will have difficulty to check-in or out for their truant colleagues. The high accuracy results will help improved security of attendance, improved employee performance, ensures fast and easy retrieval of data, easy monitoring of staff, and prevent impersonation in the attendance logs.
Keywords: Control mechanisms; Digitization; Biometric systems; Fingerprint scanner
Employee Clocking System (ECS) is an electronic process of monitoring the attendance in the work environment to minimize economic, time, productivity and loss revenue due to employee absenteeism, lateness and truancy. Attendance monitoring and evaluation have traditionally been approached using time clocks and timesheets (the manual way of taking employee attendance). However, attendance monitoring and evaluation goes beyond only attendance, but it ensures efficient time utilization which maximizes and motivates employee attendance. Employee Clocking System is the easiest way to keep track of employees’ attendance and productivity in industrial organization, business organizations and volunteer groups. Employee Clocking System is useful in terms of workforce analysis, day-to-day monitoring of attendance, maintaining statutory registers, leave records, calculation of overtime and transferring information to the payroll system .
The existing employees’ attendance system requires the employees to manually log the attendance sheet every time they come to the office, and when they close . Typically, such a system lacks automation because it is not an electronic system and might generate a number of problems. These problems
may include the time unnecessarily consumed by the workers to find and sign their name on the attendance sheet and the fact that the attendance sheet may get misplaced or kept away from employees due to suspected wrong activities. The development and implementation of this system will help organizations to manage their employees’ attendance systematically. The system has a database that contains employee’s information, and it will help the admin to manipulate data and update the database [2,3].
In today’s business transactions, it is always expected that the clients authenticate themselves for services rendered to them with control mechanisms such as identity card, ATM card, driving license, health card and so on. Carrying different cards and remembering passwords for different services is a complex issue for individuals and organizations . Secure and effective identity and access control system plays a vital role in the successful deployment of an Employee Clocking System. To make the identification and access control mechanism safe and reliable for authentication, the Employee Clocking Systems must have integrated biometric data as an added feature. The move towards the digital era is being accelerated every hour globally to meet the evolving digitization and smart system development. Biometrics
technologies verify identity through characteristics such as
fingerprints, faces, iris, retinal patterns, palm prints, voice, and
hand-written signatures and so on. This biometric authentication
present fingerprint as the most common and popular biometric
used in automatic personal identification [5,6].
Theories, as well as models, provide a basis that guides
research and interpretation of research results . In research,
theories are formulated to assist in explaining, predicting, and
understanding phenomena. In biometric systems, according
to [8,9], staff attendance is taken electronically with the help of
a Biometric fingerprint system, and all the records saved for
subsequent operations. [10,11] argued vehemently that, biometric
systems are an authentication method. As suggested in [7,12,13],
biometric systems identify people by recognizing one or several
physical characteristics. It is one of the future main solutions for
providing authentication. Fingerprints are considered to be the
best and fastest method for biometric identification because they
are secure to use, unique for every person and do not change in
one’s lifetime [11,12]. Therefore, this study is concerned with the
implementation of a biometric fingerprint authentication system
which is an automated method of verifying a match between two
human fingerprints for validating identity.
Employee clocking system using fingerprint biometric
identification technique employs an automated system to
calculate staff attendance and do further calculations of daily and
monthly attendance summary in order to reduce human errors
during calculations. In essence, our system can be employed in
curbing the problems of ghost names (people whose names are on
the payroll and receiving pay yet are not delivering any services
to the institution), the lateness of workers to their various posts,
impersonation and missing working periods in any institution.
The system will also improve the productivity of any institution if
Biometric Resource-Based Theory
The study was anchored in the Resource-Based Theory (RBT).
There is evidence from research that supports the RBT  that
argues that organizations compete in a dynamic and changing
business environment. Firms can attain and achieve a sustainable
competitive advantage through their employees, according to .
This can be realized when organizations have a pool of human
resources that cannot be imitated or substituted by their rivals or
The RBT as a foundation of competitive advantage is
embedded in the utilization of a bundle of valuable resources that
are at the disposal of the firm. It is important that firms have to
identify the major potential resources. These resources should
be valuable, rare inimitable and non-substitutable among the
competitors of the firm  in the field that they operate in. Firms’
resources must be valuable in order to make firms adopt valuecreating
strategies. The firm should outperform its competitors
or minimize the weaknesses that it may have . The RBT as a
foundation for the competitive advantage of firms’ is embedded
mainly in the use of tangible or intangible resources that firms
may have [3,15]. The RBT looks at the firm’s internal operational
environment as an important driver that can create a competitive
advantage for the firm.
The RBT assumes that an organization is made up of unique
capabilities and resources as a foundation for a firm’s strategy to
compete and be profitable and also have a competitive advantage
over its competitors. According to Hitt, Ireland and , firms can
use the resources at their disposal and capabilities to enhance
their operational performance. In order to be competitive, firms
should ensure that they carry out their activities in an integrated
approach. Firms should also adopt strategies that distinguish
them from other firms in the areas that they operate in. As a result,
organizations need to explore their frameworks if they envisage
remaining relevant in the context of the competitive global
environment. Organizations are striving to achieve a competitive
advantage, and they should put into consideration that true
competitive advantage requires the resources of an organization
to be valuable, rare, inimitable and non-substitutable as pointed
out by [5,9].
The key aspect of the Resource-Based Theory is that firms have
to identify their main resources that can make the firms to achieve
and sustain a competitive advantage against their competitors
. A resource has to be valuable to organizations like UENR,
Sunyani is expected to make optimum use of time and the human
resources that they have by ensuring that employees work fully
for the scheduled time to enable UENR to enhance its operational
performance in the delivery of administrative services.
Computerized Biometric Employee Clocking System
and Operational Performance
When computerized biometric employee clocking systems
are being designed, it is important to ensure that physiological
and behavioural features are taken into consideration .
The ultimate performance of the biometric system will depend
on how well the physiological and behavioural features were
considered in the biometric system design. The features that
need to be considered include the uniqueness of individual
users, permanence, acceptance, and hardness of the system and
levels of fulfilment . Biometrics systems help in effective
attendance management which helps in increasing employee
or workers’ productivity and generate time and overhead cost
savings to enhance the organizations’ performance by utilizing
computerized time management system to track employee time
and attendance [8,13]. Attendance timing management helps in
guiding our methods of managing working hours. The actions that
are taken to enhance efficiency was based on the principle of time
Fingerprint Level 1 and Level 2 Features Enhancement
to Improve Quality of Image
Fingerprint recognition is one of the exciting and complex
image processing problems, which requires a constant and
continuous contribution to new research from the research
community. Even though the face recognition is automatic
pattern recognition system and controlled by the computer, the
performance of the system is directly dependent on the quality
of the fingerprint images and the quality of the image capturing
device . Level 1 feature comprises of the orientation of the
fingerprint, core-centre from which ridge ending and ridge
pattern named and delta location-point on the friction ridge and
distinction of finger versus palm. As shown in Figure 1, Level 1
Features’ examples include Simple Arch, Tented Arch, Right Loop,
Left Loop, Composite Whorl, Concentric Whorl, Imploding Whorl,
Press Whorl, Spiral Whorl, Peacock’s- Eye Whorl and Variant
Whorl . Loop pattern Ridges enters from either side of the
impression or pattern, re-curves or touches an imaginary line
drawn from delta to the core and terminates on the same side
from where it is originated. In Arch, pattern ridges start from
one side of the fingerprint pattern to another side without doing
backwards turn. Whorl pattern consists of series of circles which
starts from an arbitrary point and ends at the same point [7,11,12],
with only Level 1 features, fingerprint recognition systems neither
recognize the image nor identify or verify the image . Level 1
feature is mainly used for classification, verification, filtering, and
The primary purpose of Level 1 features- ridge pattern or
flow and orientation in Figure 1 above, are mainly used for image
enhancement and orientation purpose, which will improve the
quality of fingerprints. If the image contains noisy regions, it is
difficult to define the orientation of the image. Image enhancement
techniques are essential or necessary because the image captured
through a sensor or optical device is not assured quality [5,18].
Fingerprint image enhancement is technically done by improving
the quality of ride pattern or increasing the consistency of ridge
orientation, which means level 1 feature, is exposed and analyzed.
Ridge ending and ridge bifurcation or minutiae points are level 2
features. Still, some other features like line unit, line fragment, eye,
and hook also can be extracted and studied, which are referred to
as low-level fingerprint features . The low-level features are
shown in Figure 2.
The research was localized at the UENR campus, as a case study.
A web application was designed and installed on three computers.
Each computer had a fingerprint scanner device attached to the
computer to receive or reject fingerprints images. In this study,
1000 participants (population) were selected for the fingerprint
experiment. The participants included students, teaching and
non-teaching staff. The design phase of the employee clocking
system integrated the biometric fingerprint scanner to a web
application [20,21]. The web application is a common platform
for all the fingerprint devices which connect to a single database
[22,23]. It involved dividing the whole system into modules and
defining the relationship among the constituent modules. The topdown
design approach was employed, which involved dividing the
system into subsystems or modules, and each subsystem is further
divided into even smaller subs. This process of division is repeated
until each module is sufficiently small enough to be conveniently
coded as an independent entity that performs a clearly defined
The population of the study
A total population of 1,000 employees and students at
UENR were selected randomly to participate in the biometric
fingerprint experiment. The distribution of the population has
been represented in Table 1.
Sampling Procedure and Sample Size
The sampling process has been divided into two phases.
The first phase randomly selected 500 students. The system is
envisaged to be used in the classroom to monitor students’ class
attendance. The second phase was a careful selection of teaching
and non-teaching staff. In all these phases, the availability of the
participants for the experiment was taken into consideration.
Table 1 represents the sample size for the research.
The sample size has been calculated through Slovin’s formula
 by using a confidence level of 85%.
In the Slovin’s formula, N is the total population, e is the error
of tolerance, and n is the sample size. The total population consists
of 7,200 participants selected from the University. With the help of
Slovin’s formula, 1,000 sample size has been calculated to conform
to the population segmentation with a response rate of 98%.
Research Design and Analysis
The study has been designed to improve employee attendance
at the universities and other related organizations. The employee
clocking system comprises of a database, web application  and
the finger. The fingerprint’s Software Development Kit (SDK) we
used to design the web application, and the database  includes
by using SPSS software and M.S. Excel and visual studio. Values
from the tables generated by the SPSS were also tested in Excel
using formulas. The system [24,25] consists of the attendance
software installed on an HP 630 Laptop with 64-bit Operating
System (Windows 10), 1 Terabyte Hard disk, 16 Gigabytes RAM
and 4 Gigahertz (Intel Pentium Processor) and a fingerprint
scanner. The developed application was installed on three
computers and the fingerprint scanner as well, to communicate
with the application software. The computer has been labelled
System 1, System 2 and System 3. The developed system was
tested, and every bug detected was corrected for system worked
Flowchart of the Employee Clocking System
The flowchart presented in Figure 3 shows the visual
representation of the sequence of steps and decisions needed to
perform a process involved in the ECS. Each step in the sequence
is noted within a diagram shape. Connecting lines and directional
arrows link steps. This allows readers to view the flowchart and
logically follow the process from beginning to end. The presented
ECS flowchart is a robust algorithm with proper design and
construction, which communicates the steps in the ECS processes
very effectively and efficiently.
Program’s Structure Analyses and GUI Construction
The web application and database [21-23] were developed
and implemented in a working environment to enable the users
to communicate with the database through the fingerprint
scanner. Graphical User Interface (GUI) of the web application
was built up to facilitate the collection of biometric features of
the participants. The GUI consists of username, age, time in, time
out, among other numbers of controls (textboxes, combo-boxes,
button, etc.). The list of all properties and methods for all controls
which allowed the system to communicate with a fingerprint
scanner was the Application Programming Interface (API). A set of
controls was used to reach the desired purpose, the functionality
of the application, including Labels, Text boxes, Combo Boxes, Data
Grid, Buttons, Group Boxes, Panels, Tab controls, etc. All of these
controls were available in the application and were fitted to the
corresponding forms, which were to be filled by the participants.
Windows Forms text boxes are used to get input from the
user or to display text. The TextBox control was generally used for
editable text, although it can also be made read-only. Text boxes
can display multiple lines, wrap text to the size of the control, and
add basic formatting. The Windows Forms ComboBox control
was used to display data in a drop-down combo box. By default,
the ComboBox control appears in two parts: the top part is a text
box that allows the user to type a list item. It can be noticed that
almost all of the controls are grouped and placed on a special field
(platform) and can switch from one group to another by clicking
on the responding titles.
ECS Performation Evaluation Process
The employee clocking system requires an employee to
establish his or her identity in the system in the first instance.
This process is called enrollment; the employee has to present his
or her fingers for imaging or scanning. The captured biometrics
was processed by the ECS and encryption algorithms. A biometric
template was generated as a result of processing, which was stored
in a database and associated with identity data of that person.
This procedure is part of the enrollment step. The next step is an
authentication process where the employee verification of his or
her identity, fingerprints are re-scanned, processed, and the new
template is compared with the existing one in the database. The
matching algorithm returns a match in case of acceptance or nomatch
in case of rejection.
Performance Evaluation Matrics of ECS
One of the essential factors in the success of a biometric
system is its accuracy. The performance is a measure of how well
the system can correctly match the biometric information from
the same person and avoid falsely checking biometric information
from different people. The measurement of biometric accuracy
is usually expressed as a percentage or proportion, with the
data coming from simulations, laboratory experiments, or field
trials. This study used both percentage and proportion in its
performance evaluation. There are four principal measures of
a) True Acceptance Rate (TAR) / True Match Rate
(TMR): This measure represents the degree that the biometric
system can match the biometric information from the same person
correctly. Developers of biometric systems attempt to maximize
b) False Acceptance Rate (FAR) / False Match Rate
(FMR): This measure represents the degree or frequency where
biometric information from one person is falsely reported to
match the biometric data from another person. Developers
attempt to minimize this measure.
c) True Rejection Rate (TRR) / True Non-Match Rate
(TNMR): This measure represents the frequency of cases when
biometric information from one person is correctly not matched
to any records in a database because that person is not in the
database. Developers attempt to maximize this measure.
d) False Rejection Rate (FRR) / False Non-Match Rate
(FNMR): This measure represents the frequency of cases when
biometric information is not matched against any records in a
database when it should have been matched because the person
is, in fact, in the database. Developers attempt to minimize this
Other standard biometric accuracy measurements essential
for determining the final success of the ECS systems deployed in
our work are:
a) Failure-to-enrol rate (FTE): the proportion of the user
population for whom the biometric system fails to capture or
extract usable information from the biometric sample.
b) Failure-to-acquire rate (FTA): the proportion of
verification or identification attempts for which a biometric
system is unable to capture a sample or locate an image or signal
of sufficient quality.
In addition to these error metrics, other performance metrics
are used to ensure the operational use of biometric systems such
a) average enrollment time
b) average verification time
c) average and maximum template size
d) the maximum amount of memory allocated
Verification System Performance Metrics
False rejection rate (FRR): the proportion of authentic users
that are incorrectly denied. If a verification transaction consists of
a single attempt, the false reject rate would be given by:
FRR(τ ) = FTA + FNMR(τ ) * (1− FTA)
False acceptation rate (FAR): the proportion of impostors
that are accepted by the biometric system. If a verification
transaction consists of a single attempt, the false accept rate
would be given by:
FAR(τ ) = FMR(τ ) * (1− FTA)
Equal Error Rate (EER): this error rate corresponds to the
point at which the FAR and FRR cross (a compromise between FAR
and FRR). It is usually used to evaluate and to compare biometric
authentication systems. The more the EER is near to 0%, the
better is the performance of the target system.
Identification System Performance Metrics
Identification rate (I.R.): The identification rate at rate r is
defined as the proportion of identification transactions by users
enrolled in the system in which the user’s correct identifier is
among those returned.
False-negative identification-error rate (FNIR): The
proportion of identification transactions by users enrolled in the
system in which the user’s correct identifier is not among those
returned. For an identification transaction consisting of one
attempt against a database of size N, it is defined as:
FNIR(τ ) = FTA + (1− FTA) * FNMR(τ )
False-positive identification-error rate (FPIR): The
proportion of identification transactions by users not enrolled in
the system, where an identifier is returned. For an identification
transaction consisting of one attempt against a database of size N,
it is defined as:
The three systems were connected to the same database,
which simultaneously checks the fingerprint images with those
in the databases. The testing of the system was done three weeks
after the employee enrollment was done. The test ensured the all
the participants had their biodata tested with the data stored in
the database. After four days of testing, we realized that some of
the participants had difficulty with the authentication process. To
measure this anomaly, we used the performance evaluation matrix
to determine the accuracy of the test. True Acceptance Rate, False
Acceptance Rate, True Rejection Rate and False Rejection Rate
were used to test the results.
Other parameters, such as receiver operating characteristics
and Cumulative match characteristic, were represented graphically
to determine the performance of the system, which directly ensure
that we achieve the objectives of this work.
Receiver operating characteristic curve (ROC): the
representation of the rate of FMR as well as FAR, thus, accepted
impostor attempts on the x-axis against the corresponding rate
of FNMR as well as FRR, thus, rejected genuine attempts on the
y-axis plotted graphically as a function of the decision threshold.
An illustration of a ROC curve has been presented in Figure 4.
Cumulative match characteristic curve (CMC): this is
the graphical representation of results of the identification test,
plotting rank values on the x-axis and the probability of correct
identification at or below that rank on the y-axis. The CMC curves
have been given in Figure 5.
It is interesting to note that the study found a few errors (0.5%
to 1.5%) caused by filing fingerprints with the wrong personal
information than it found false acceptances by the biometric
system. It also found a strong relationship between the quality
of the fingerprint images stored at enrollment and the accuracy
during verification comparisons.
The best images had TAR=98% at FAR=0.01%, while the
worse images had TAR=47% at FAR=0.01%. Finally, this study also
showed the value of combining two fingerprints at verification
time. When this was done, the accuracy increased to TAR=99.7%
when FAR=0.01%, as indicated in Tables 2-4.
Overall, fingerprint matching accuracy suggests that the
performance was quite good with high-quality fingerprint images.
Caution was appropriate; however, because the results were from
a real-world experiment, the actual accuracy much better than the
results obtained by [11,13,18].
The truancy of employees has affected the productivity of many
organizations. This situation has resulted in the loss of revenue,
among many other adverse effects. This paper sought to introduce
a biometric employee clocking system to help overcome the high
level of truancy in workplaces. The results of the experiment we
conducted indicate a high accuracy in our system with TAR value
of 99.7%. This accuracy rate is much better than the results other
researchers obtained. The implication of the good accuracy is that
employees will have difficulty to check-in or out for their truant
colleagues. The high accuracy results will help improved security
of attendance, improved employee performance, ensures fast
and easy retrieval of data, easy monitoring of staff, and prevent
impersonation in the attendance logs. The automated process,
with the aid of fingerprint biometrics, does not give room for
impersonation. Once an employee has been enrolled, it cannot be
verified by another person.
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