Automated Eye-Pattern Recognition Systems
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#1
21-09-2008, 11:23 PM


Privacy of personal data is an illusion in todayâ„¢s complex society. With only passwords, or Social Security Numbers as identity or security measures every one is vulnerable to invasion of privacy or break of security. Traditional means of identification are easily compromise and enyone can use this information to assume anotherâ„¢s identity. Sensitive personal and corporate information can be assessed and even criminal activities can be performed using another name. Eye pattern recognition system provides a barrier to and virtually eliminates fraudulent authentication and identity privacy and safety controls privileged access or authorised entry to sensitive sites, data or material. In addition to privacy protection there are myriad of applications were iris recognition technology can provide protection and security. This technology offers the potential to unlock major business opportunities by providing high confidence customer validation. Unlike other measurable human features in the face, hand, voice or finger print, the patterns in the iris do not change overtime and research show the matching accuracy of iris recognition systems is greater than that of DNA testing. Positive identifications can be made through glasses, contact lenses and most sunglasses. Automated recognition of people by the pattern of their eyes offers major advantages over conventional identification techniques. Iris recognition system also require very little co-operation from the subject, operate at a comfortable distance and are virtually impossible to deceive. Iris recognition combines research in computer vision, pattern recognition and the man-machine interface. The purpose is real-time, high confidence recognition of a persons identity by mathematical analysis of the random patterns that are visible with in the iris. Since the iris is a protected internal organ whose random texture is stable throughout life, it can serve as a ???living passwordâ„¢ that one need not remember but one always carries. .
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madireddyswetha
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#2
03-02-2010, 05:11 PM

plse send me more information on AUTOMATED EYE PATTERN RECOGNITION
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seminarbuddy
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#3
03-02-2010, 08:39 PM

Automatic Eye Detection and Its Validation
The accuracy of face alignment affects the performance of
a face recognition system. Since face alignment is usually
conducted using eye positions, an accurate eye localization algorithm is therefore essential for accurate face recognition.

Automatic Eye Detection
The main uses of eye detection are:
-detect the existence of eyes
-accurately locate eye positions
: active and passive eye detection systems are available today.
- Passive methods directly detect eyes from images within
visual spectrum and normal illumination. Some early work
extracts distinct features from eyes localization.
-Active eye detection methods use special types of illumination. Under
IR illumination, pupils show physical properties which can
be utilized to localize eyes The advantages of active eye detection methods are that they are very accurate and
robust.But they need special lighting to work.

Eye Localization Algorithm
To better represent eyes the statistically learning of
discriminate features to characterize eye patterns is proposed. learning of probabilistic classi?ers to separate eyes and non-eyes is also studied. Multiple classi?ers are then combined in AdaBoost to form a robust and accurate eye detector.

Discriminant Features for Eye Detection
A training sample is containing the image intensity
data, and the sample label as the choosing criteria is taken. In this paper,
One criteria to extract a good feature for pattern classi-
?cation is that the feature can minimize the estimated
Bayes error function The Fisher discriminant analysis (FDA) is equiv-
alent to Bayesian classi?er if assuming Gaussian distribu-
tion and equivalent priors and covariance matrix for each class.

Feature Selection and Classi?er Construction with AdaBoost
The AdaBoost selects the critical features and train weak classi?ers as well
as updates the sample weights. The main task in the AdaBoost is the selection of features
to learn weak classi?ers. more powerful discrim-
inant features is used instead of rectangular Haar features to im-
prove eye detection accuracy. To train a robust eye detector, training
data was collected from various sources. 500 pairs of eyes were collected from a database for study. training. only a left eye detector is trained In application,
due to the symmetry of eyes.

Eye Localization
The eye localization method follows a hierarchical princi-
ple. a face is detected first, then eyes are located inside
the detected face. Ada boost is used here too. multiple eyes detected around the pupil center. The ?nal eye localization is the average of the multiple detection results.

Eye Detection Validation
In one kind of validation experiments, a set of manually labeled eye positions were used. The performance of our eye detector is characterized by the eye detection rate and
eye localization error. The localization error is measured as the Euclidean distance between the detected eye posi-
tions and manual eye positions. In the second experiment, performance of eye detection was measured based on
its in?uence on face recognition accuracy of two standard
baseline methods: PCA and PCA together with LDA.

full report:




.pdf   eye recognition.pdf (Size: 542.83 KB / Downloads: 503)
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mohit_812
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#4
13-02-2011, 10:40 AM

plz snd ppt. report....Smile
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seminar class
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#5
19-02-2011, 04:04 PM


.docx   Automated Eye-pattern vision system.docx (Size: 25.48 KB / Downloads: 89)
Automated Eye-Pattern Recognition Systems
What is Iris?

• The colored part of the eye is called the iris.
• It controls light levels inside the eye.
• The iris is embedded with tiny muscles that dilate and constrict the pupil size.
• The iris is flat and divides the front of the eye from the back of the eye.
• Its color comes from microscopic pigment cells called melanin.
Eye Diagram
Characteristics of Iris

• Has highly distinguishing texture.
• Right eye differs from left eye.
• Twins have different iris texture.
• Iris pattern remains unchanged after the age of two and does not degrade overtime or with the environment.
• Iris patterns are extremely complex than other biometric patterns.5
History of Eye-Pattern Recognition System
• In the mid-1980s, ophthalmologists Leonard Flom and Aran Safir realized that no two patient’s irises were alike.
• In 1987, the pair were issued the so-called Flom patent, which has given the company they founded, Iridian Technologies, dominance in the iris-recognition market. 6
Need of Eye-Pattern Recognition Technology
• Illusion for Data Privacy.
• Passwords, or Social Security Numbers can be cracked easily.
• Eye pattern recognition system virtually eliminates fake authentication and identity privacy and safely controls authorized entry to sensitive sites, data or material.
• Iris Pattern is most distinguished than any other facial feature and do not change overtime and research show the matching accuracy of iris recognition systems is greater than that of DNA testing.
• Iris recognition system is easy to operate, comfortable and is virtually impossible to deceive.
• Since the iris is a protected internal organ whose random texture is stable throughout life, it can serve as a living password that one need not remember but one always carries. .
Operating Principle
• An iris-recognition algorithm first has to identify the approximately concentric circular outer boundaries of the iris and the pupil in a photo of an eye.
• The set of pixels covering only the iris is then transformed into a bit pattern that preserves the information that is essential for a statistically meaningful comparison between two iris images.
• The mathematical methods used resemble those of modern lossy compression algorithms for photographic images.
• In the case of Daugman's algorithms, a Gabor wavelet transform is used in order to extract the spatial frequency range that contains a good signal-tonoise ratio considering the focus quality of available cameras.
• The result is a set of complex numbers that carry local amplitude and phase information for the iris image.
• In Daugman's algorithms, all amplitude information is discarded, and the resulting 2048 bits that represent an iris consist only of the complex sign bits of the Gabor-domain representation of the iris image.
• Discarding the amplitude information ensures that the template remains largely unaffected by changes in illumination and virtually negligibly by iris color, which contributes significantly to the long-term stability of the biometric template.
• To authenticate via identification or verification , a template created by imaging the iris is compared to a stored value template in a database.
• If the Hamming distance is below the decision threshold, a positive identification has effectively been made.
• A practical problem of iris recognition is that the iris is usually partially covered by eyelids and eyelashes.
• In order to reduce the false-reject risk in such cases, additional algorithms are needed to identify the locations of eyelids and eyelashes and to exclude the bits in the resulting code from the comparison operation.
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seminar class
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#6
18-03-2011, 09:41 AM

Presented By:
Prashant Khandelwal


.ppt   07S11A0428.ppt (Size: 1.23 MB / Downloads: 88)
What is Iris?
1.The colored part of the eye is called the iris. It controls light levels inside the eye.
2.The iris is embedded with tiny muscles that dilate and constrict the pupil size.
3.The iris is flat and divides the front of the eye from the back of the eye.
4.Its color comes from microscopic pigment cells called melanin.
Eye Diagram
Characteristics of Iris

1.Has highly distinguishing texture.
2.Right eye differs from left eye.
3.Twins have different iris texture.
4.Iris pattern remains unchanged after the age of two and does not degrade overtime or with the environment.
5.Iris patterns are extremely complex than other biometric patterns.
History of Eye-Pattern Recognition System
1.In the mid-1980s, ophthalmologists Leonard Flom and Aran Safir realized that no two patient’s irises were alike.
2.In 1987, the pair were issued the so-called Flom patent, which has given the company they founded, Iridian Technologies, dominance in the iris-recognition market.
Need of Eye-Pattern Recognition Technology
1.Illusion for Data Privacy.
2.Passwords, or Social Security Numbers can be cracked easily.
3.Eye pattern recognition system virtually eliminates fake authentication and identity privacy and safely controls authorized entry to sensitive sites, data or material.
4.Iris Pattern is most distinguished than any other facial feature and do not change overtime and research show the matching accuracy of iris recognition systems is greater than that of DNA testing.
5.Iris recognition system is easy to operate, comfortable and is virtually impossible to deceive.
6.Since the iris is a protected internal organ whose random texture is stable throughout life, it can serve as a living password that one need not remember but one always carries. .
Procedure
 Infrared Eye imaging
 Creation of an Iris code
 Iris recognition
Infrared Eye imaging
The iris scan process begins with a photograph. A specialized camera, typically very close to the subject, not more than three feet, uses an infrared imager to illuminate the eye and capture a very high-resolution photograph. This process takes 1 to 2 seconds.
Creation of an Iris code
The picture of eye first is processed by software that localizes the inner and outer boundaries of the iris. And it is encoded by image-processing technologies.
Iris recognition
In less than few seconds, even on a database of millions of records, the iriscode template generated from a live image is compared to previously enrolled ones to see if it matches to any of them.
Techniques used
 Iris Localization
 Iris Normalization
 Image Enhancement
Iris Localization
1.Both the inner boundary and the outer boundary of a typical iris can be taken as circles.
2.But the two circles are usually not co-centric. The inner boundary between the pupil and the iris is detected.
3.The outer boundary of the iris is more difficult to detect because of the low contrast between the wo sides of the boundary.
4.The outer boundary is detected by maximizing changes of the perimeter- normalized along the circle.
Iris Normalization
1.The size of the pupil may change due to the variation of the illumination and the associated elastic deformations in the iris texture may interfere with the results of pattern matching.
2.Since both the inner and outer boundaries of the iris have been detected, it is easy to map the iris ring to a rectangular block of texture of a fixed size.
Image Enhancement
1.The original image has low contrast and may have non-uniform illumination caused by the position of the light source.
2.These may impair the result of the texture analysis.
Advantages
1.Iris is an internal organ which is highly transparent and sensitive membrane. It is more persistent than fingerprints as they tend to fade under manual labor.
2.The iris has a fine texture which is determined randomly during embryonic gestation. Even genetically identical individuals have completely independent iris textures, whereas DNA is not unique for the about 0.2% of the human population who have a genetically identical twin.
3.An iris scan is similar to taking a photograph and can be performed from about 10 cm to a few meters away.
Disadvantages
Iris scanning being a new technology is incompatible with most electronic gadgets present.
1.Iris recognition is very difficult to perform at a distance larger than a few meters and without proper cooperation of the person.
2.As with other photographic biometric technologies, iris recognition is susceptible to poor image quality.
3.Equipments used for scanning are very expensive.
Applications
1.United Arab Emirates Iris Guard's Homeland Security Border Control has been operating an expellee tracking system in the United Arab Emirates since 2001, when it launched a national border-crossing security initiative.
2.One of three biometric identification technologies internationally standardized by ICAO for use in future passports .
3.Iris recognition technology has been implemented by BioID Technologies SA in Pakistan for UNHCR repatriation project and implimentation to control aid distribution for Afghan refugees.
4.At Schiphol Airport, Netherlands, iris recognition has permitted passport-free immigration since 2001.
5.In a number of US and Canadian airports, as part of the NEXUS program that facilitates entry into the US and Canada for pre-approved, low-risk travelers.
6.In several Canadian airports, as part of the CANPASS Air program that facilitates entry into Canada for pre-approved, low-risk air travelers.
Conclusion
1.Iris recognition has proven to be a very useful and versatile security measure.
2.It is a quick and accurate way of identifying an individual with no chance for human error.
3.Iris recognition is widely used in the transportation industry and can have many applications in other fields where security is necessary.
4.Iris recognition will prove to be a widely used security measure in the future.
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