face recognition system.
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rawat.nishant967
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#1
26-01-2011, 11:37 AM


hi,
i want to know more about face recognition system,how it works and a demo of synopsis for it as early as possible.
I will be thankful to you.
Your user[/size][/font]
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seminar surveyer
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#2
27-01-2011, 10:51 AM

hi
please go through the following thread for more on face recognition system.

topicideashow-to-face-recognition-technology-a-seminar and presentation-report
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manoos1413
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#3
07-07-2011, 05:43 PM

please send me full report and ppt of face reconginition system
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smart paper boy
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#4
08-07-2011, 09:57 AM

to get information about the topic face recognition technology full report ppt, and related topics refer the link bellow

topicideashow-to-face-recognition-technology-a-seminar and presentation-report

topicideashow-to-face-recognition-technology-a-seminar and presentation-report?page=4

topicideashow-to-seminar and presentation-report-on-face-recognition-technology

topicideashow-to-FACE-RECOGNITION-TECHNOLOGY-SEMINAR-REPORT--28533

topicideashow-to-face-recognition-technology--27390

topicideashow-to-face-recognition-technology-a-seminar and presentation-report?page=5

topicideashow-to-face-recognition-technology-a-seminar and presentation-report?page=2

topicideashow-to-face-recognition-technology-a-seminar and presentation-report?page=3

topicideashow-to-facial-recognition-system

topicideashow-to-advances-in-the-face-detection-and-recognition-technologies

topicideashow-to-face-recognition-using-laplacian-faces--5867

topicideashow-to-biometric-face-recognition

topicideashow-to-face-recognition-using-neural-networks-download-seminar and presentation-report

topicideashow-to-face-recognition-using-the-techniques-base-on-principal-component-analysis-pca
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seminar paper
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#5
15-02-2012, 02:04 PM

to get information about the topic face recognition technology full report ppt, and related topics refer the link bellow

topicideashow-to-face-recognition-technology-a-seminar and presentation-report

topicideashow-to-face-recognition-technology-a-seminar and presentation-report?page=4

topicideashow-to-seminar and presentation-report-on-face-recognition-technology

topicideashow-to-FACE-RECOGNITION-TECHNOLOGY-SEMINAR-REPORT--28533

topicideashow-to-face-recognition-technology--27390

topicideashow-to-face-recognition-technology-a-seminar and presentation-report?page=5

topicideashow-to-face-recognition-technology-a-seminar and presentation-report?page=2

topicideashow-to-face-recognition-technology-a-seminar and presentation-report?page=3

topicideashow-to-facial-recognition-system

topicideashow-to-advances-in-the-face-detection-and-recognition-technologies

topicideashow-to-face-recognition-using-laplacian-faces--5867

topicideashow-to-biometric-face-recognition

topicideashow-to-face-recognition-using-neural-networks-download-seminar and presentation-report

topicideashow-to-face-recognition-using-the-techniques-base-on-principal-component-analysis-pca
Reply
seminar flower
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#6
29-05-2012, 04:16 PM

Face recognition Systems


.doc   Face recognition Systems.doc (Size: 55 KB / Downloads: 19)

ABSTRACT:

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition Systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given no frontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the Virtual frontal view from a given no frontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a no frontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the no frontal face image to obtain many overlapped local patches. Then, the linear Regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated.

REPORT

Add:
In this module, User can add their part of photo in the database. So Photo, fingerprint and part of their photo are in a database for each user. By selecting part of the photo, it will be show the full image.
View All:
This module maintains the full fingerprint photos. Admin has the rights to view all the photos. And he has the rights to delete and manage. Fingerprint profile management for each user is managed here..
Search:

If the user select fingerprint mean, he can see the full image of their photo or if the user select one part of the photo mean, remaining part will be retrieved from database and finally show the full image of the person. Thus the user can recognize the image. Original image will be recognized in this module.

Existing System:

It has been studied for more than three decades. The state-of-the-art recognition technologies can achieve very high accuracy under restricted environment, such as frontal faces under controlled lighting conditions. However, most of current face recognition systems fail under uncontrolled cases since they are pretty sensitive to pose, lighting, occlusion, aging, and other variations. Especially, pose problem has been one of the bottlenecks for most current face Recognition technologies. Pose difference induces large variation of the appearance Even for the same person. The distinction is often more remarkable than that caused by the difference of identity under the same pose. Therefore, the typical appearance-Based methods many approaches have been proposed to deal with pose problem. Among them, The view-based methods are widely used. For instance, view-based Eigen face had been proposed to extend the Eigen face to handle the pose problem. One disadvantage of the view-based method is that it usually needs multiple face images with different poses for each subject, which is often impractical for real-world applications.

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seminar flower
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#7
11-08-2012, 02:20 PM

Face Recognition System


.pptx   Presentation on Face Recognition System.pptx (Size: 761.99 KB / Downloads: 13)

Introduction

A Facial Recognition System is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.
One of the ways to do this is by “Comparing selected facial features from image and a facial database”.
Swiss European surveillance: facial recognition and vehicle make, model, color and license plate reade
Facial recognition systems are computer-based security systems that are able to automatically detect and identify human faces.
These systems depend on a recognition algorithm, such as eigenface or the hidden Markov model.
The system measures nodal points on the face, such as the distance between the eyes, the shape of the cheekbones and other distinguishable features.
These nodal points are then compared to the nodal points computed from a database of pictures in order to find a match.

Requirement Analysis

Users requirements are gathered.
SRS document is prepared.
Functional requirements are to be considered.
Hardware and Software requirements are to be considered.

Feasibility Study

Feasibility Study is done so that an ill-conceived system is recognized early in definition phase.
This phase is really important as before starting with the real work of building the system, it was very important to find out whether the idea thought is possible or not.
After doing analysis, we came to conclusion that our project and implimentation was feasible.

System Design

System design is the process of developing specifications for a candidate system that meets the criteria established in the system analysis.
Major step in System Design is the preparation of the input forms and the output reports in a form applicable to the user.
The main aim of the System Design is to make the system user friendly.

Schema Design

RELATIONAL MODEL:

Certain rules are followed in creating and relating database in relational database.
This governs how to relate data and prevent data redundancy in the databases.
The first set of rules called relational rules ensure that the database is a relational database.
The second is called the normalization rule simplifies the database and reduce the redundancy of data.
Testing and Test Plans

VALIDATION CHECKS:

It can be performed by any piece of the software. If the user tries to do unauthorized operations, the appropriate error message is generated by the system.

METHODS OF VALIDATION:

1. Presence Check: Checks that data has been entered into the field and it has not been left blank.


2. Type Checks: Checks that an entered value is of particular type. E.g. checks that a field is varchar2, number etc.

3. Length Checks: Checks that an entered value is no longer than a particular number of characters.

4. Format Checks: Checks that an entered value is of a particular format. E.g. date format must be “mm-dd-yy” format.

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