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Assignment on Finger Print based
attendance system
Group members:
Roll Nos.:
Shrey Raturi
Sharvari Rautmare
Parag Rengade
Sahil Sankhla
Urmila Sathe
Ganesh Shanker
Nandini Sharma
Amey Tore
Sakharam Thorat
Fingerprint identification is one of the most well known and publicized
biometric identification system. Because of their uniqueness &
consistency over time, fingerprints have been used for identification
over a century, more recently becoming automated due to advancement
in computing capabilities. Fingerprint identification is popular because
of the inherent ease in acquisition, the numerous sources (10 fingers)
available for collection and the various established sources of collections
(by law enforcement and immigration.)
So, here we are using the fingerprint identification technique for
maintaining the attendance record. We plan to maintain a record of the
prints of the various students in the database, and they shall be matched
and marked present when they swipe their fingerprints across the
A fingerprint usually appears as a series of dark lines that represent the
high peaking portion of friction ridged skin, while the valleys between
these ridges appears as white space and are the low shallow portion of
the friction ridged skin. Fingerprint identification is based primarily on
the minutiae, which are the locations and directions of the ridge endings
and bifurcations (splits) along a ridge path.
The above images are examples of fingerprint feature: a. two types of
minutiae and b. example of other detailed characteristic, sometimes used
during the automatic classification and minutiae extraction process.
The types of information that can be collected from a fingerprint’s
friction ridge impression include the flow of the friction ridges (level 1),
the presence or absence of features along the individual friction ridge
paths and their sequence (level 2), and the intricate detail of a single
ridge(level 3). The recognition is usually based on the first and second
level of detail or just the latter.
Other terms used in relation to a fingerprint:
Block Diagram
Figure 4
User Interface:
The user interface provides mechanisms for a user to indicate his/her
identity and input his/her fingerprints into the system.
System database:
The system database consists of a collection of records, each of which
corresponds to an authorized person that has access to the system.
Enrollment Module:
The task of enrollment module is to enroll persons and their fingerprints
into the system database.
Authentication Module:
Each record contains the following fields which are used for
authentication purpose:
1. User name of the person
2. Minutiae templates of the person’s fingerprint
3. Other profile information
Hardware Architecture
A variety of sensor types – optical, capacitive, ultrasound and thermal
are used for collecting the digital image of fingerprint surface. Optical
sensors take an image of the fingerprint, and are the most commonly
used sensors today.
The capacitive sensor determines each pixel value based on the
capacitance measured, which is made possible because an area of
air(valley) has significantly less capacitance than an area of
finger(friction ridged skin).
Other fingerprint sensors capture images by employing high frequency
ultrasound or optical devices that use prisms to detect the change in light
reflectance related to the fingerprint.
Thermal scanners require a swipe of a finger across a surface to measure
the difference in temperature over time to create a digital image.
We shall now move on to the details of hardware we will be employing:
To implement the attendance system, we shall be making use of two
technologies: Embedded systems and Biometrics.
Firstly discussing about Biometrics we are concentrating on Fingerprint
FIM 3030N:
This module we are using as a scanner. It is a high voltage module
fingerprint scanner. It has in-built ROM, DSP and RAM. In this we can
store up to 100 users fingerprints. This module can operate in 2 modes
they are Master mode and User mode. We will be using Master mode to
register the fingerprints which will be stored in the ROM present on the
scanner with a unique id.
When this module is interfaced to the microcontroller (8051), we will
be using it in user mode. In this mode we will be verifying the scanned
images with the stored images. When coming to our application the
images of the students will be stored in the module with a unique id. To
register their attendance the students have to scan their image which is
then verified with the image present in fingerprint module and their
attendance is registered for that day.
This scanner is interfaced to 8051 microcontroller through max232
enabling serial communication. By using this controller we will be
controlling the scanning process. After the scanning has been completed
the result is stored in the microcontroller. By simply pressing a switch
we can get the list of absentees for that day.
Block Diagram:
Figure 5
This system uses regulated 5V, 500mA power supply.
7805 three terminal voltage regulator is used for voltage regulation.
Bridge type full wave rectifier is used to rectify the ac output of
secondary of 230/12V step down transformer.
Power Supply
+5V, 500mA Regulated Power Supply
LED 5mm, 16 X 2 LCD
Biometric Sensor
Storage Capacity
Up to 100 finger print images
Image Registration
Through Serial Communication
Software Architecture
Finger print matching:
Given two (input and template) sets of features originating from two
fingerprints, the objective of the feature matching system is to determine
whether or not the prints represent the same finger.
Fingerprint matching has been approached from several different
strategies, like image-based, ridge pattern-based, and point (minutiae)
pattern-based fingerprint representations. There also exist
graph-based schemes for fingerprint matching.
Image-based matching may not tolerate large amounts of non-linear
distortion in the fingerprint ridge structures. Matchers critically relying
on extraction of ridges or their connectivity information may display
drastic performance degradation with a deterioration in the quality of the
input fingerprints.
We, therefore, believe that point pattern matching (minutiae matching)
approach facilitates the design of a robust, simple, and fast verification
algorithm while maintaining a small template size.
The matching phase typically defines the similarity (distance) metric
between two fingerprint representations and determines whether a given
pair of representations is captured from the same finger (mated pair)
based on whether this quantified (dis)similarity is greater (less) than a
certain (predetermined) threshold.
The two main categories of fingerprint matching techniques are minutiae
based matching and pattern matching. Pattern matching simply
compares two images to see how similar they are. It is usually used in
fingerprint systems to detect duplicates. The most widely used
recognition technique, minutiae based matching relies on the minutiae
points (refer figures 1 and 2). Specifically the location and direction of
each point.
Pattern-based (or image-based) algorithms:
Pattern based algorithms compare the basic fingerprint patterns (arch,
whorl, and loop) between a previously stored template and a candidate
fingerprint. This requires that the images be aligned in the same
orientation. To do this, the algorithm finds a central point in the
fingerprint image and centers on that. In a pattern-based algorithm, the
template contains the type, size, and orientation of patterns within the
aligned fingerprint image. The candidate fingerprint image is graphically
compared with the template to determine the degree to which they match
and a match score is generator.
Minutia Feature extraction based algorithms:
These algorithms use minutiae features on the finger. The major Minutia
features as shown in Fig.2 of fingerprint ridges are: ridge ending,
bifurcation, and short ridge (or dot). The ridge ending is the point at
which a ridge terminates. Bifurcations are points at which a single ridge
splits into two ridges. Short ridges (or dots) are ridges which are
significantly shorter than the average ridge length on the fingerprint.
Minutiae and patterns are very important in the analysis of fingerprints
since no two fingers have been shown to be identical.
Flow chart of the minutiae extraction algorithm (Figure 6):
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