-By T.E Comp•Muneer Ahmed•Israt Ali•Dilip Kumar•Akash Patel•Hardik Patil•Viraj Samant

FINGERPRINT RECOGNITION

PROCESS & ANALYSIS

Contents

1. Definition.2. Introduction.3. Features.4. Uses.5. Advantages.6. Applications.7. Fingerprint

Recognition Gadgets.

8. Types of fingerprint.9. Characteristics of

Fingerprint.10. Variations of

Fingerprint.

11. Fingerprinting Sensors.

12. Algorithms.13. Different Techniques of

Fingerprinting. 14. Requirements for

Fingerprint Processor.15. Block Diagram of

Fingerprint Processing System.

16 . Fingerprint Mechanism using DIP.

17. Steps in Fingerprint Processing.

18. Conclusion. 19. References.

Definition

A fingerprint in its narrow sense is an impression left by the friction ridges of a human finger.

Introductioni. Fingerprint Identification/Recognition is one of the most well-

known & publicized biometrics.

ii. Because of their uniqueness & consistency over time, fingerprints have been used for Identification for over a century & recently becoming automated.

iii. The concept of fingerprint came in 19Th century by Sir Francis Galton, which were known as Galton Points.

iv. In 1969, it was a major push by FBI (Federal Bureau of Investigation) & CIA (Central Intelligence Agency) officials in United States to develop a completely “Automated Fingerprint Identification Process” to track Criminals, Smugglers & keep their records as an Identification to track them in American Airports & Ports.

v. Hence, making this attempt famous & overwhelming around the world.

Features

Unique - The ridges and their characteristics of our fingers are unique. Each person has distinct and unique ridges on finger, sole, and palm.

Uses Personal Identification:-The patterns of fingerprint are

extensively modified but the quantity, characteristics, and position of the ridges remained unchanged.

Eg:- Fingerprint in an ID-Card.

Criminal Cases:- During investigations on criminal cases - Fingerprinting is the most affordable and at the same time the best way to identity a criminal. The presence of fingerprints in the crime scene is the only way that will prove who was the criminal. This is because a fingerprint alone is enough conclusive evidence. 

Advantages & Applications Advantages To prevent stealing of

identity - The fingerprints are important to be included in some important documents such as passports, social security cards, bank accounts, driving licenses, and others to avoid the use or access of unauthorized persons.

Applications

Fingerprints are important

in manufacturing

biometrics-based electronic

gadgets such as – Finger printing systems. Security systems door. Locking door access

control systems. Attendance fingerprint

systems. Digital fingerprints of

security systems.

Fingerprint Recognition Gadgets

Types of fingerprint Visible prints :- It is also called patent prints. It can be

seen when blood, dirt, ink or grease on the finger come into contact with a smooth surface and leave a friction ridge impression that is visible without development.

Latent prints :- They are not apparent to the naked eye. They can be made sufficiently visible by dusting, fuming or chemical reagents.

Impressed prints :- It also called plastic prints. They are visible and can be viewed or photographed without development.

Characteristics Variations

Fingerprinting Sensors

A fingerprint sensor is an electronic device used to capture a digital image of the fingerprint pattern. The captured image is called a live scan. This live scan is digitally processed to create a biometric template (a collection of extracted features) which is stored and used for matching. This is an overview of some of the more commonly used fingerprint sensor technologies. :-

Optical:- Optical fingerprint imaging involves capturing a digital image of the print using visible light. This type of sensor is, in essence, a specialized digital camera.

Ultrasonic:- Ultrasonic sensors make use of the principles of medical ultra-sonography in order to create visual images of the fingerprint. Unlike optical imaging, ultrasonic sensors use very high frequency sound waves to penetrate the epidermal layer of skin.

Capacitance:- Capacitance sensors utilize the principles associated with capacitance in order to form fingerprint images.

Passive capacitance:- A passive capacitance sensor uses the principle outlined above to form an image of the fingerprint patterns on the dermal layer of skin.

Active capacitance:- Active capacitance sensors use a charging cycle to apply a voltage to the skin before measurement takes place.

Algorithms

Matching algorithms are used to compare previously stored templates of fingerprints against candidate fingerprints for authentication purposes. In order to do this either the original image must be directly compared with the candidate image or certain features must be compare.

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. The candidate fingerprint image is graphically compared with the template to determine the degree to which they match.

Different Techniques of Fingerprinting Traditional Technique:-

The traditional or most common technique of fingerprinting is taking impressions of a person's hand with the help of ink. Pressing fingers covered in ink, on to a paper is the way of obtaining fingerprints.

Digital Scanning Technique:-A sensitive touch-pad is used to capture the fingerprints of a person or a suspect in this method. The impression of fingerprint recorded on the touch-pad is then compared with thousands of impressions with the help of software .

Lifting Technique:-In this technique, oil from hands which are left behind are captured by means of powders made from resinous polymers. This technique is specially used in Crime Investigations.

Laser Technique:-The laser technique is one of the most useful for capturing fingerprints. In this fingerprinting technology, the fingerprints from many different surfaces can be lifted by means of laser.

Bullet Fingerprinting :-

This technique is considered as a sure shot method of fingerprint identification, because even wiping and washing of the surface cannot remove the sweat gland deposits completely. It is possible to visualize the fingerprint.

Requirements for Fingerprint Processor Speed. Size of the fingerprint identification database Power consumption. The core architecture of a processor: A dual

multiply-accumulate (MAC) or single-MAC core should be considered for this application.

Bus architecture. Power management integration. Others: Other important considerations include

dedicated hardware for different addressing modes, loop control and execution control and peripheral integration.

Block Diagram of Fingerprint Processing System.

Fingerprint Mechanism using DIP A popular and reliable way to compare fingerprints is to analyze the minutiae of the fingerprint. To do

so, a system must first capture the image of a fingerprint and process the image to make it easy for image analysis. After the analysis, the minutiae will be extracted and saved in a template format. The system must then store the data of the minutiae to be used for future comparison.

Fingerprint sensorsBased on the fingerprint processing diagram, a sensor is the “front-end” of the system, playing an import role: it captures the image of the fingerprint. The two types of popular sensors used in fingerprint analysis are the optical and swipe sensors. Here are basic descriptions of how they operate. 

Optical sensor:-An optical fingerprint sensor captures a digital image of the fingerprint by using visible light. The following describes the operation principle of an optical sensor.

First, the light source inside of a sensor will emit light to illuminate the surface of the finger. The light reflected from the finger passes to a solid-state pixels sensor (either a charge-coupled device

[CCD] or complementary metal oxide semiconductor [CMOS] image sensor), which captures a visual image of the fingerprint. On the path of the light, a specially designed lens will be used.

Swipe sensor:-A swipe sensor is a type of active capacitance sensor. Here is how an active capacitance sensor works.

First, the active capacitance sensors will apply a voltage to the skin. This will generate an electrical field in the space between the skin and sensor.

Since the electrical field between the finger-skin and sensor follows the pattern of the ridges in the dermal skin layer, the effective capacitance will be measured across this field.

The distances between skin and sensors can then be calculated mathematically by using this equation: C = ε0*εr*(A/d); here “d” represents the distance.

Based on the calculated distances across the field, the fingerprint image can be mapped.

OPTICAL SENSOR. SWIPE SENSOR.

Continued… Fingerprint processing

After an image of a fingerprint is captured, a sequence of image processing algorithms will be applied to the captured image. In fingerprint authentication applications, two main types of technologies are being used: one is called minutiae based, and the other is called image based.

Minutiae are the special spots of a fingerprint that show the changing of the print. These spots have been predefined and categorized.  Two main features of minutiae, which are extracted from these spots:

Ridge ending. Bifurcation. However, the minutiae are not limited to these two features. In a minutiae-based system, the goal is to find the minutiae in the captured

fingerprint image and compare them with fingerprints that are in the database. In order to extract the minutiae successfully, the fingerprint images must be preprocessed, which usually involves computationally-intensive image processing algorithms.

The digital image signal processing steps include:

Segmentation and filtering. Contrast enhancement. Orientation calculation. Gabor filtering. Binarization. Thinning. Feature extraction.

Continued…. Segmentation and filtering:- The main purpose of segmentation is

to get the “good” area of a captured fingerprint image, then separate this valid fingerprint from the image background. Some filtering can be applied to the image to filter out the noise in the image.

Contrast enhancement:- After segmentation, the image is subjected to gray stretch to increase the global contrast of the image. Because the skin of an entire finger has a similar color, the more interesting parts of the fingerprint and the less interesting areas have a very low level of contrast.

Mathematically, this type of operation is transformation. Orientation calculation:- There are different implementations for

mapping the orientation of a fingerprint. The most popular algorithm to map the orientation of fingerprints is the gradient-based approach. The gradient (x, y) at point [∇ x,y] of I (an image) is a two-dimensional vector [ x(x, y) y(x,y)].∇ ∇Mathmatically, gradient is the first derivative of the image, the x ∇ ∇and y are the derivatives on ∇ X and Y directions, respectively. 

Continued…. In a fingerprint system, to numerically calculate the x(x, y)and y(x,y), a ∇ ∇

popular method is to use the Sobel operator. The following is a 3x3 Sobel mask:

The gradient can be calculated as following:

Continued…. Then the gradient’s direction, angle θ, can be calculated as:

These calculations will be applied across the fingerprint image, and an

orientation map will be created.  Gabor filtering:-  A Gabor filter is defined as a two-dimensional Gaussian

function multiplied by a sinusoidal plane wave function:

Continued…. Here the xθ and yθ are the point coordinate [x,y] rotated (90-θ) degrees, defined

as:

θ represents the orientation. f represents the frequency of the ridge-valley-ridge pattern; it can

be the reciprocal of the width of ridge-valley measurement. σx and σy are the standard deviations of the Gaussian envelope

on x and y directions, respectively.

Binarization :- The goal of binarization is to convert the gray-level image to binary level “1” or “0.” In other words, this space-changing operation converts the image to black or white with no levels in between.

Continued…. Thinning:- After the binarization, the ridges and valleys are in black

and white, respectively, but the width of lines may be wider than one pixel. To further reduce the complexity of minutiae extraction, a thinning algorithm will be applied to the image.

Feature extraction. Figure 5 illustrates the fingerprint image for each processing step during the entire fingerprint analysis flow. After these steps of signal processing, we will obtain the final fingerprint image. The minutiae-based features, such as ridge ending and bifurcation, will be found and extracted from the final image.

Conclusion

Our fingerprints are unique and permanent from birth until death.

Our Fingerprints Never Lie as Our Faces Do.

Only if we know the unique knowledge to this science.

This is because fingerprint has its own language and it is hard to understand the truth it reveals.

References www.fbi.gov www.biometrics.gov www.altavista.com www.fpvte.nist.gov

THANK YOU….!!!!