Face
recognition, both by humans and machine, is developing as an active research
area. This area presence a critical survey of existing literature on human and
machine recognition of faces. In order to better design machine based faced
recognition, it is necessary to understand how human perceive faces. The
primary task at hand, given still or video images, requires the identification
of one or more persons using a database of stored face images. To do this the
face must segmented and extracted from the scene, where upon it can be
identified and matched.
•
Human and
Machine Recognition of Faces
Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, face recognition technology (FRT) has numerous commercial and law enforcement applications. These applications range from static matching of controlled format photographs such as passports, credit card, photo ID’s, driver’s licenses and mug shots to real time matching of inspection video images presenting different constraints in terms of processing requirements. Although humans seem to recognize faces in cluttered scenes with relative ease, machine recognition is a much more demoralizing task. A general statement of the problem can be formulated as follows; given still or video images of a scene identify one or more persons in the scene using a stored database of faces. Available collateral information such as race, age and gender may be used in narrowing the search. The solution of the problem involves segmentation of faces from cluttered scenes, extraction of features from the face region, identification and matching, the generic face recognition task thus posed is a central issue in problems such as electronic line up and browsing through a database of faces.
• Problem Description
The problem with the present system is same as problems encountered in any manual file processing system. The existing system does not support the cropped images of criminals. The existing system is not suitable in some cases such as if a witness can identify only a part of the criminal. Present system uses some algorithms for identifying criminal faces which are difficult to process. Finally the existing system doesn’t always produce better results in identifying the criminals by their images.
Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, face recognition technology (FRT) has numerous commercial and law enforcement applications. These applications range from static matching of controlled format photographs such as passports, credit card, photo ID’s, driver’s licenses and mug shots to real time matching of inspection video images presenting different constraints in terms of processing requirements. Although humans seem to recognize faces in cluttered scenes with relative ease, machine recognition is a much more demoralizing task. A general statement of the problem can be formulated as follows; given still or video images of a scene identify one or more persons in the scene using a stored database of faces. Available collateral information such as race, age and gender may be used in narrowing the search. The solution of the problem involves segmentation of faces from cluttered scenes, extraction of features from the face region, identification and matching, the generic face recognition task thus posed is a central issue in problems such as electronic line up and browsing through a database of faces.
• Problem Description
The problem with the present system is same as problems encountered in any manual file processing system. The existing system does not support the cropped images of criminals. The existing system is not suitable in some cases such as if a witness can identify only a part of the criminal. Present system uses some algorithms for identifying criminal faces which are difficult to process. Finally the existing system doesn’t always produce better results in identifying the criminals by their images.
• Feasibility
study
Depending
on the results of initial investigation the survey is now expands to a more
detailed feasibility study. “Feasibility study” is a test of a system proposal
according to its work ability, impact of the organization, ability to meet needs
and effective use of the resources.
It
focuses on these major questions
• What are the user’s demonstrable
needs and how does a candidate system meet
them?
• What resources are available for
given candidate system?
• What are likely impacts of the
candidate system on the organization?
• Whether it is worth to solve the
problem?
During
feasibility analysis for this project, following primary areas of interest are
to be considered. Investigation and generating ideas about a new system does
this.
Steps in
feasibility analysis
Seven steps are involved in the
feasibility analysis are:
• Prepare system flowcharts.
• Enumerate potential proposed system.
• Define and identify characteristic
of proposal system.
• Determine and evaluate performance
and cost effective for each proposed system.
• Weight system performance and cost
data.
• Select and best-proposed system.
• Prepared and report final project
directive to management.
Modules:
• Login form
• Main Menu form
• Getting photo from file
• Getting photo from camera
• Storing details in database
• Select cropped images
• Displaying details along with the image
• Updating details
• Deleting detail from the database
• Face detection
• Face recognition
Software Requirements:-
• Matlab
• Front End: Matlab (GUI)
• Back End: Mathimatica
• Database : Ms Access/ Oracle
• Adobe Photoshop
Operating system Environment:
•
Windows 7 or Above
Hardware Environment:-
•
Processor: Core i3 CPU
•
RAM: 4 GB
•
Hard disk: 80 GB
•
CCTV
Camera
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