Assignment on Finger Print based attendance system

Group members:

Roll Nos.: Name:

G-57 Shrey Raturi

G-58 Sharvari Rautmare

G-59 Parag Rengade

G-60 Sahil Sankhla

G-61 Urmila Sathe

G-64 Ganesh Shanker

G-66 Nandini Sharma

G-72 Amey Tore

G-70 Sakharam Thorat

IntroductionFingerprint 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 scanner.

Concept

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 ArchitectureA 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 scanning.

FIM 3030N:

Specifications:

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.

Interfacing:

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.

Specifications:

Microcontroller : AT89S52

Power Supply : +5V, 500mA Regulated Power Supply

Display : LED 5mm, 16 X 2 LCD

Crystal : 11.0592MHz

Biometric Sensor : FIM3030N

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 existgraph-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):