Tuesday, March 4, 2008

Fingerprint Recognition System


With the advent of electronic banking, e-commerce, and smart cards and an increased emphasis on the privacy and security of information stored in various databases, automatic personal identification has become a very important topic. Accurate automatic personal identification system is now needed in a wide range of civilian applications involving the use of passports, cellular telephones, automatic teller machines, and driver licenses. Traditional knowledge-based [password or personal identification number (PIN)] and token-based (passport, driver license, and ID card) identifications are prone to fraud because PIN’s may be forgotten or guessed by an imposter and the tokens may be lost or stolen.


Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capability to reliably distinguish between an authorized person and an imposter.


A biometric system can be operated in two modes:
1) Verification mode &

2) Identification mode.


A biometric system operating in the verification mode either accepts or rejects a user’s claimed identity while a biometric system operating in the identification mode establishes the identity of the user without claimed identity information. Among all the biometrics (e.g., face, fingerprints, hand geometry, iris, retina, signature, voice print, facial thermogram, hand vein, gait, ear, odor, keystroke dynamics, etc.) fingerprint-based identification is one of the most mature and proven technique.


A fingerprint is the pattern of ridges and valleys on the surface of the finger. The uniqueness of a fingerprint can be determined by the overall pattern of ridges and valleys as well as the local ridge anomalies a ridge bifurcation or a ridge ending, called minutiae points. The critical factor in the widespread use of fingerprints is in its satisfying performance (e.g., matching speed and accuracy) requirements of the emerging civilian identification applications. Some of these applications (e.g., fingerprint- based smart cards) will also benefit from a compact representation of a fingerprint.


The popular fingerprint representation schemes have evolved from an intuitive system design tailored for fingerprint experts who visually match the fingerprints. These schemes are either based on predominantly local landmarks (e.g., minutiae-based fingerprint matching systems) or exclusively global information. The minutiae-based automatic identification techniques first locate the minutiae points and then match their relative placement in a given finger and the stored template.


The fingerprint image may be obtained from a thumb pad fingerprint scanner device scanning at 500 dpi. A good quality fingerprint contains between 60 and 80 minutiae, but different fingerprints have different number of minutiae. The minutiae extracted from the fingerprint image is stored in the database when working on enrollment mode (identification) & in the verification mode the extracted minutiae is compared with those already stored in the database .If a matching above a threshold level occurs then the person is recognized by the system.


Prominent applications of fingerprint recognition system include criminal identification, security systems in organizations, license generation, home security, automated attendance systems in colleges and universities.

References :

1. Filterbank-Based Fingerprint Matching

Anil K. Jain, Fellow, IEEE, Salil Prabhakar, Lin Hong, and Sharath Pankanti

2. Fingerprint Image Enhancement: Algorithm and Performance evaluation

Lin Hong, Student Member, IEEE, Yifei Wan, and Anil Jain, Fellow, IEEE

3. Fingerprint Enhancement in the Singular Point Area

Sen Wang, Student Member, IEEE, and Yangsheng Wang]

4. Minutiae Detection Algorithm for Fingerprint Recognition

Virginia Epinoso – Duro, Polytechnic University of Catalonia.

5. Feature Extraction—A Pattern for Information Retrieval.

Dragos¸-Anton Manolescu

6. A hybrid fingerprint matcher .Arun Rossa, Anil Jain, James Reisman

7. Digital Image Processing: Concepts, Algorithms & Scientific Applications

By Bernd Jahne

8. Image Processing In C By Dwayne Philips

9. Digital Image Processing Techniques By Anil Kumar Jain

10. Feature Extraction Using a Chaincoded Contour ,Representation of

Fingerprint Images

Venu Govindaraju, Zhixin Shi

CEDAR, Department of Computer Science and Engineering,

John Schneider

11. Software Engineering – A practitioners approach – Roger Pressman

12. IEEE Online journals

13. www.google.com

Explore Bhopal : An Information Kiosk on Bhopal

This software was made as a part of our Management Information Systems project.

OBJECTIVES:-
· Provide information about Bhopal.
· Give assistance to user to find the information of a particular entity.
· To make map system

INTRODUCTION:-
The sole aim of this software is to give the user all information about Bhopal. Database of this software consists of information about markets, hotels and restaurants, educational Institutes and coaching institutes, hospitals and nursing homes, entertainment centers, tourist places and industries in Bhopal. This user friendly software will answer most queries about Bhopal. we have made different modules of data so that user can easily navigate through the software.

FEATURE HIGHLIGHTS:-
- User Friendly Front-End
- Dynamic Looks
- Large Data Storage Capacity
- Easy to Administer and Configure the Software
- Excellent Security Features
- Efficient links with internet

HARDWARE REQUIREMENTS:-
- IBM / IBM Compatible System (486,Pentium-I,P-II)
- 32 MB RAM (Memory)
- Color Monitor (with 800 x 600 Pixels Resolution)
- Mouse
- IBM / IBM Compatible Keyboard
- CD-ROM Drive
- Color Printer

SOFTWARE REQUIREMENTS:-
- Windows 95/98/2000/XP (Operating System)
- Microsoft Office
- Microsoft Visual Basic (Version-6.0)
- Oracle database

SOFTWARE MODULES:-

Ø MARKET
Ø ENTERTAINMENT
Ø HOSPITALS
Ø EDUCATION
Ø BANKS & FINANCIAL INSTITUTIONS
Ø TRAVEL & TOURISM
Ø RESERVATION & MAPS
Ø INDUSTRIES
Ø HOTELS & RESTAURANTS


MARKET:
It is supposed to provide categorical listing of various market places and the items for supporting daily chores. Markets are classified on the following lines:
Ø Computer peripherals and systems/ hardware/software vendors and dealers.
Ø Dress materials.
Ø Automobile showrooms
Ø Departmental stores including co-operatives
Ø Medicine stores
Ø Local outlets: company
Ø Beauty parlours

ENTERTAINMENT:
Ø Theatres
Ø Cinema Halls
Ø Amusement parks
Ø Sports and recreation
Ø Discotheques
Ø Clubs

HOSPITALS:
Ø Public/ Private.
Ø Nursing Homes.
Ø Maternity Homes

EDUCATION:
Ø Colleges (Engineering/Commerce/Medical/Arts).
Ø Schools.
Ø Coaching centres.

BANKS AND FINANCIAL INSTITUTIONS:
Ø Co-operative banks.
Ø Industrial development banks.
Ø Agricultural banks
Ø Financial institutions.

TRAVEL & TOURISM:
Ø Tourist spots.
Ø Travel agents.
Ø Conducted tours.

RESERVATION & MAPS:
Ø Railways.
Ø Airways.
Ø Roadways.
Ø Maps.

INDUSTRIES:
Ø Public sector.
Ø Private sector.
Ø Co-operative sector.

HOTELS & RESTAURANTS:
Ø Economy/Family.
Ø Bar.

Anaconda : The Game of an Intelligent Snake

** This game in the form of a technical paper was presented by Moksh Walia & myself in Confluence 2K4, Infofiesta 2K4 , the techno fest organised at National Institute of Technology, Rourkela , where we won the 2nd prize. **
_______________________________________________________________
In this game there is a snake named as ‘Anaconda’ that is very hungry and can eat endlessly. The snake is represented by a sequence of small squares or circles and arrow keys control the movement of the snake in any direction. Whenever snake eats some food its body gets enlarged by two bits by its head. If the head of snake hits its body or any of the circular hurdles placed randomly on the screen in its path then it dies ending the game as a consequence. The game starts with some initial graphics and user will have to select some options then the game is played on the output screen.

We have introduced many hurdles in the path of snake. Food for snake appears for some time on a particular place after that it appears at some other place, the snake has to eat this within this period of time and get points for it. As the time proceed snake keeps on eating and its size keeps on increasing. As the size increases it becomes more and more difficult to play. User can select level at which he wants to play, with increase in level, speed with which snake will move will increase and user will get more points for every food snake eats. The goal to play game is to earn more and more points as much as user can. While playing the game user can see his present score on the screen.

User can also select mazes; different mazes have different type of hurdles. By default game will run without any maze and at the slowest of the speeds available unless user select some level. In no maze condition snake can traverse out of the screen and will reappear from other side of the screen. The game is a simple simulation of the snake game available in Nokia mobile handsets.

About the technical specifications of the software:

· This software runs on windows95/98/NT/XP platform & at least 64 Kb of System memory with a monitor supporting a resolution of 640X480 using 16 colors.
· This game has been designed in C Programming language using graphics.
· It has approximately 1000 LOC.
Reference : http://www.soft-computing.de/def.html

Maths : The invisible hand behind Music

** Rohan Jain & I presented this paper at Intellect 2k3 organised by Manipal Institute of Technology, Manipal, Karnataka in April, 2003 **
-------------------------------------------------------------------------------------------------
Mathematics and music play very different roles in society. However, they are more closely related to each other than they are more commonly perceived to be.Behind the art of music lies an exact science, wherein a practical application of mathematics is found, in the form of series, progressions, arithmetic mean and so on. This wonderful relationship between music and mathematics is as ancient as it defies understanding. It nevertheless makes for an interesting study. Many great mathematicians have also been great lovers of music. In the ancient period the Greeks had discovered serious relations between these two aspects.Many systems of tunings developed earlier but as days progressed OCTAVES become the most popular of them all which also supported the theory of consonance and dissonance. The theory of GOLDEN RATIO takes into account musical compositions. A music composer basically needs to deal with lots of mathematics, be it counting, patterns, sequences, ratio proportions, geometry, equivalent fractions, progressions etc. Mathematics may be freed from its utter serious implications when an emblem of music is applied to it. As far as mathematical simulation is concerned we can develop a set of rules to define a fact that MUSIC CAN ACT AS a DNA OF MATHEMATICAL FUNCTIONS, which can be implemented using a high level structured computer language such as “C”.

For many people, mathematics is an enigma. Characterized by the impression of the numbers and calculations taught at school, it is often accompanied by feelings of rejection and disinterest, and it is believed to be strictly rational, abstract, cold and soulless. Music, on the other hand, has something to do with emotion, with feelings, and with life. It is present in all daily routines. Everyone has sung a song, pressed a key on a piano, blown into a flute, and therefore made music. It is something people can interact with; it is a way of expression and a part of everyone’s existence.

The motivation for investigating the connections between these two apparent opposites therefore is not very obvious, and it is unclear tins what aspects of both topics such a relationship could be seeked. Moreover, if one accepts some mathematical aspects in music such as rhythm and pitch, it is more difficult to imagine any musicality in mathematics. The accountability and the strong order of mathematics do not seem to coincide with an artistic pattern. However, there’re different aspects, which indicate this sort of relationship. But mathematics and music do not form such strong opposites as they are commonly considered to be, but that; there are connections and simulations between them, which may explain why some musicians like mathematics and why mathematicians generally love music.

A very interesting aspect of mathematical concepts in musical compositions is the appearance of Fibonacci numbers and the theory of golden section. The former is the infinite sequence of integers named after Leonardo de Pisa, a medieval mathematician. Its first two numbers are both 1 whereas every new number of the sequence is formed by the addition of the two proceedings (1, 1, 2, 3, 5, 8, 13, 21, 34….). However the most important feature in this context is that the sequence of Fibonacci ratios converges to the constant limit, called the Golden Ratio, Golden proportion or section (0.61803398….)

More common is the geometric interpretation of the golden section: A division of a line into two unequal parts is called a golden if the relation of the length of the whole line to the length of the bigger part is the same as the relation of the length of the bigger part to the length of the smaller part. Due to its consideration as well balanced beautiful and dynamic, the golden section has found various applications in arts specially in painting and photography, where important elements often divide a pictures length or width (or both) following the golden proportion, However, such a division is not necessarily undertaken consciously, but results from an impression of beauty and harmony. Diverse studies have discovered that this same concept is also very common in musical compositions. The golden section- expressed by Fibonacci ratios –is either used to generate rhythmic changes or to develop a melody line. There is a single principle that underlines all musico-mathematical relations: An arithmetic progression in music corresponds to geometric progressions in mathematics; that is, the relation between the two is logarithmic.

A “C” SIMULATION OF MUSIC IN MATHS

While establishing an amalgamation of math and music and contemplating on their relation from music to math point of view, it won’t be a redundancy to try to confer upon mathematical function an attribute of music. Intuitively, if such notion can be made to exist, then every such function should have a character signature of music. But to define such an abstract relation a fixed set of rules is quite necessary. Of course a platform for defining such relations could be a computer language where it can be implemented in an exhaustive way.

Now any mathematical function has a typical range of values that can be easily expressed within a limit by multiplying with typical whole numbers or fractions. For example if a typical scale of three octaves is considered, each of 12 notes, then a range of 36 notes are obtained. Any kind of music is based on these three octaves. Thus associating SA with 1,RE with 2, RA with 3 and so on, a musical scale can be defined.
Basically if three functions are considered: -
1. Logarithmic Function.
2. Polynomial Function
3. Trigonometric Function

It seems that their structure resembled musical scores, so as an experiment let’s see what they sound like when the following rules are defined to convert the values of the function to a range of 1 to 36.

Considering the polynomial function first: Its positive values may range from 1 to 32767(the limit for integer value in ‘C’). By dividing it into three ranges: i.e. 1 to 36, 37 to 1296, &1297 to 32767.Any value of the function in the first region can be directly processed to get the corresponding note. A value in the second range can be divided by 36(a whole number) to get the value again in the first range. For the third range the values can be divided by 910(another whole number) to get the values within the first range and the subsequent sound output. A question arises as to why the negative values of the function are to be neglected when they can also add to the music DNA of the polynomial. Well they could be converted to a positive one by multiplying with –1 and given the same treatment as to their positive counterparts.

A logarithmic function can have the highest value of 10.39.(log (32767) ) which when multiplied with 3151 gives the range of values from 1 to 32767, which can then be treated the same way as that of polynomial.

A totally different treatment lies in store for the trigonometric functions: Since the trigonometric functions are periodic so let’s define a base when they obtain a zero value. Let this base be 18. Any negative values will be treated in the range 1 to 18 and positive values within 18 to 36.

1.SINE wave: Within the range 0 to 360 degrees, a sine function gradually rises to a value 1.00 from 0.0 and falls from 1. 00 to 0.00 in the range 90 to 180degrees.so the rise may be simulated as a rise from 18 to 36 then fall from 36 to 18 then go down from 18 to 1 and then again rise from 1 to 18.hence the cycle gets completed. Based on the same lines we can define the following tables for the remaining tables: -

2. COSINE TABLE: -

DEGREE RANGE - SIMULATION RANGE
0 to 90 - 36 to 18
90 to 180 - 18 to 1
180 to 270 - 1 to 18
270 to 360 - 18 to 36

In a very similar way we can also simulate the remaining three ratios, so that they represent a particular note pattern. Is it "music"? I guess that's for you to decide. It is richly structured, with underlying themes that on the one hand seem to repeat but on the other hand are interestingly unpredictable, teasing your mind as the piece progresses.

All these aspects of mathematical patterns in sound, harmony and composition do not convincingly explain the outstanding affinity of mathematicians for music. Being a mathematician does not mean discovering numbers everywhere and enjoying only issues with strong mathematical connotations. The essential relation is therefore presumed to be found on another level. Whatever, links between music and mathematics exist, both of them are obviously still very different disciplines, and one should not try to impose one on the other. It would be wrong to attempt explaining all the shapes of music by mathematical means as well as there would be no sense of studying mathematics only from musicological point of view. However, it would be enriching if these relationships were introduced into mathematical education in order to release mathematics from its often too serious stringencies. It is important to show people that mathematics, in one way, is as much as art as it is a science. This probably would alter its common perception, and people would understand better its essence and its universality. This task, however, will certainly not be completed by the end of this century.


References : Various papers on the discussion of relation between music and maths.

Electronic Smell

** Niraj Kumar & I presented this paper at Felicity '04, held at IIIT, Hyderabad in February, 2004. We won the 3rd prize. **
**Moksh Walia & I presented this paper again at Troika '04, held at Delhi College of Engineering, New Delhi in March 2004. We won the 2nd prize. **
____________________________________________________________________
It is now possible to add a new sense of smell to the Internet. This is done through the virtual nose called the e-nose. An electronic nose is not a replacement for people, it is a supplement. The "electronic nose" is a relatively new tool that may be used for safety, quality, or process monitoring, accomplishing in a few minutes procedures that may presently require days to complete

The electronic nose consists of two components,
(1) an array of chemical sensors (usually gas sensors) and
(2) a pattern-recognition algorithm.
The sensor array "sniffs" the vapors from a sample and provides a set of measurements; the pattern-recognizer compares the pattern of the measurements to stored patterns for known materials. Gas sensors tend to have very broad selectivity, responding to many different substances. This is a disadvantage in most applications, but in the electronic nose, it is a definite advantage. Although every sensor in an array may respond to a given chemical, these responses will usually be different. In recent years, electronic noses have been sniffing out landmines, detecting contraband drugs, sensing for chemical and biological weapons, identifying batches of spoiled food, and even showing promise for aiding in the diagnosis of diseases like lung cancer and pneumonia. These interesting devices are designed to mimic the ability of the human nose to detect very small quantities of odorants. E-noses can also detect chemicals that have no odor, such as toxic carbon monoxide. Compared to the senses of sight, hearing, and touch, scientists know relatively little about how humans smell and taste. Designers of electronic noses have tried to mimic human noses by linking together sensors that detect a variety of volatile compounds.

The advantages of ANN-containing electronic noses over chemical sensors. The `nose' is trained on examples rather than rules, negating the need for expert description of the domain. The number of odours classified is greater than the number of sensors because the network can discriminate between patterns of activation across all the sensors. Fewer sensors are needed. Thus one can use less selective (and less expensive) sensors. Real-time odour identification. The time consuming part of the process is training of the network. Once trained the system's performance is governed by the speed of the chemical sensors. It processes new smells, despite never having having been trained on them.

Need of an electronic nose : Human noses have always been the best odor receptors distinguishing between very similar ones. Contrary to physical senses (dealing for instance with acoustic or optic mechanisms), some aspects of the human taste and olfaction physiological working principle are still unclear. Because of these intrinsic difficulties toward the understanding of the nature of these senses, only sporadic research on the possibility of designing artificial olfactory systems was performed until the end of the eighties. But as good as human noses are for chemical detection, they have drawbacks. They "fatigue" if subjected to repeated smelling tasks or strong scents, as anyone knows who has initially been shocked by an overpowering smell and then has become acclimated to the odor. The exquisite sensitivity of the nose can be defeated by a common cold, and for obvious reasons, human noses have limitations on sniffing out highly toxic compounds.The electronic noses are unbiased. They are not subject to interference by emotional states (e.g., tiredness, mood) or illness (e.g., allergies). They can be used in dangerous situations (e.g., contamination testing). They are not subject to habituation.


DIFFERENT TYPES OF SENSORS
1. The sensors used in an electronic nose can be mass transducers (such as Quartz microbalanz or QMB)

2. Chemo resistors (based on metal-oxides )
3. Chemo resistors (based on conducting polymers)
4.Some arrays comprise both types of sensors.
5. Nanomechanical Cantilevers

The artificial nose demonstrator is based on micro fabricated nanomechanical cantilever sensors - thin silicon beams - a few hundred micrometers long and one micrometer thick. Eight cantilever sensors, each is coated with a different sensor layer, are integrated in an exchangeable array. On exposure to an analyte, the analyte molecules adsorb on the cantilever’s surface. This leads to formation of interfacial stress between sensor and adsorbing layer. The bending pattern is characteristic for each analyte.


How smell sensors work : A smell sensor can be made from a quartz crystal with electrical connections and a special plastic coating. Quartz crystals are used in electronics because they can be made to vibrate at a precise frequency. A quartz crystal is what is used to control the speed of a processor in a PC. The frequency of vibration of the quartz crystal depends on its size, shape, stiffness and mass. The plastic coating on the crystal absorbs some chemicals so increases the crystals mass. The whole device is called a Quartz Crystal Microbalance (QCM) A quartz crystal can be thought of as mass on a spring.The frequency of oscillation of a mass on a spring is given by the formula: f = ½*PI*( (k/m)).Where k is the stiffness of the system in N/m, m is the mass of the system in Kg, f is the frequency of the system in Hertz.


Data Processing Methods: The signals generated by an array of odour sensors need to be processed in a sophisticated manner. The electronic nose research group has obtained considerable experience in the use of various parametric and non-parametric pattern analysis techniques. These include the use of linear and non-linear techniques, such as discriminant function analysis, cluster analysis, multi-layer perceptions, genetic algorithms, fuzzy logic, and adaptive models.


Pattern Recognition : A sensor comprises a material whose physical properties vary according to the concentration of some chemical species. These changes are then translated into an electrical or optical signal which is recorded by a device. The sensors are non-selective A chemical compound is identified by a pattern of the outputs given by the different sensors, thanks to pattern recognition methods. There is an exhaustive database which contains the information about patterns of different chemicals. The pattern now generated by the sensors and the data processor is compared with every entity of the database. If a match occurs then the chemical is recognized by the system.


Other techniques of operation: Electronic odour sensing devices have arrays of sensors that detect the presence of vapors. In this way they act as volatile chemical detectors. The sensors respond by producing electrical signals that are passed on to an artificial intelligence system programmed to interpret them.


APPLICATIONS:
Environmental Monitoring:
· Monitoring of factory emissions, air quality and household odours.
· Detection of oil leaks.
· Analysis of toxic wastes and fuel mixtures.


Medicine:
. Breath odours. The Highland Psychiatric Research Group is pioneering a
breath odour analyser for the prediction of acute schizophrenic illness in vulnerable patients; normally an extremely complicated procedure.


Body fluids. The smell of urine and blood can help in the diagnosis of liver and bladder problems.
Wounds: Smell can be an important indicator that the operation is not going well and so a remote electronic nose coupled with a local odour generator would help in the
transmission of olfactory information for medicine


Food industry applications.
- Inspection of food to test for ripening/rotting.
- Testing of packaging materials for odour containment.
- Microwave oven cooking control.
- Verifying if orange juice is natural.
- Grading whiskey and controlling fermentation.
E-nose could sniff out time of death. It could detect the time of death of a corpse by identifying odors.


Army applications. Inspecting the presence of landmines, detecting contraband drugs, sensing for chemical and biological weapons.


Thus we see that no one expects e-noses to duplicate all the capabilities of the human nose anytime soon. But they can deliver substantial benefits in situations where, given the choice, we'd prefer not to use our own sniffers. No instrument is complete without its shortcomings and an electronic nose is no exception.

Its drawbacks include:
- Difficulty in maintaining an exhaustive database of different fingerprints of chemicals.
- It may be very difficult to analyze a complex mixture of different chemicals.
- The precision of the device while analyzing similar smell is controversial.


There are numerous potential applications of electronic noses from the product and process control through to the environmental monitoring of pollutants and diagnosis of medical complaints. However, this requires the developments of application-specific electronic nose technology that is electronic noses that have been designed for a particular application. This usually involves the selection of the appropriate active material, sensor type and pattern recognition scheme. The work of the group has led to several commercial instruments, one employing commercial tin oxide sensors (Fox 2000, Alpha MOS, France) and another employing conducting polymer sensors (NOSE, Marconi Applied Technology, UK). Collaborations also exist with Osmetech (UK) and Cyrano Sciences (USA) Future developments in the use of hybrid micro sensor arrays and the development of adaptive artificial neural networking techniques will lead to superior electronic noses.

____________________________________________________________

References :
REFERENCES

1 http://www.inapg.inra.fr/ens_rech/siab/asteq/elba/sommelen.htm

2 http://www.cogs.sussex.ac.uk/lab/nlp/gazdar/teach/atc/1998/web/sloss/index.html

3 Danny Kingsley – ABC Science Online

4 Gardner J W and Bartlett P N 1999 Electronic Noses ( OUP Press, Oxford); Gardner J W and Bartlett P N (Eds) 1992 Sensors & Sensory Systems for an Electronic Nose (Dordrecht: Kluwer Academic Publishers) NATO ASI Series:
Applied Science Vol. 212 pp.327

5 Gardner J W and Bartlett P N 1994 Sensors and Actuators B 18 211-220 "A
brief history of electronic noses"'