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keyless car entry through face recognation
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keyless car entry through face recognation


.docx  REPORT.docx (Size: 2.44 MB / Downloads: 112)

INTRODUCTION

Face recognition is a form of biometric identification that relies on data acquired from the face of an individual. Now a day's face recognition systems are a need of a number of defense, security and commercial applications. Growing numbers of applications are starting to use face-recognition as the initial step towards interpreting human actions, intention, and behavior, as a central part of next-generation smart environments. Finding a face from a video frame is one of the situations where face recognition may help reasonably. Human can easily and quickly identify this variance while machine is slower and error prone. There are numbers of standard algorithm for biometrics recognition used these days. Majors are implemented using MATLAB (Matrix laboratory), Open CV and other dedicated software's using C or some other coding language. Maximum number of already developed face recognition system on FPGA uses soft core processor NIOS of Altera for implementing the system.


FPGA


A field-programmable gate array (FPGA) is an integrated circuit designed to be configured by the customer or designer after manufacturing. Instead of being restricted to any predetermined hardware function, an FPGA allows you to program product features and functions, adapt to new standards, and reconfigure hardware for specific applications even after the product has been installed in the field—hence the name "field-programmable”. The FPGA configuration is generally specified using a hardware description language (HDL).



HARDWARE AND SOFTWARE DESCRIPTION


Face recognition system is composed of three units which are input, processor and output unit. The input unit is a digital camera having NTSC (National Television System Committee) video format as standard, as the core DE_TV is only applicable to NTSC format. Now available cameras in market have both PAL and NTSC format so we can switch between them The processor used is Altera Cyclone® II 2 C35of FPGA device. The processor unit deals with the video decoding, detection of the face, recognition and data transmission to the output unit. The processor process the incoming signals from the input units and send the processed data to the output units which is the motor and VGA in this projects.


DE2 BOARD SPECIFICATIONS

The DE2 board has many features that allow the user to implement a wide range of designed circuits, from simple circuits to various multimedia projects.The following hardware is provided on the DE2 board:
• Altera Cyclone® II 2C35 FPGA device
• Altera Serial Configuration device - EPCS16
• USB Blaster (on board) for programming
• 512-Kbyte SRAM
• 8-Mbyte SDRAM
• 4-Mbyte Flash memory (1 Mbyte on some boards)
• SD Card socket
• 4 pushbutton switches
• 18 toggle switches
• 18 red user LEDs
• 9 green user LEDs


ADVANTAGES


• As compared with other biometrics systems using fingerprint/palmprint and iris, face recognition has distinct advantages because of its non-contact process. Face images can be captured from a distance without touching the person being identified, and the identification does not require interacting with the person.
• In addition, face recognition serves the crime deterrent purpose because face images that have been recorded and archived can later help identify a person


DISADVANTAGES


• Not much accurate.
• Person has to show the same pose at the time of recognition.
• As is the case for pose difference, light intensity also creates much difference for the system to recognize.


CONCLUSION

Face recognition is a both challenging and important recognition technique. It has been shown that the proposed system can be implemented at any types of automobiles and can be used at any place where face recognition or detection is needed. The proposed system can be used to detect any other thing with some amendments. Among all the biometric techniques, this approach possesses one great advantage, which is its non-intrusiveness. In this paper, we have developed a new algorithm from which we have been able detect, store, recognize and differentiate among human faces using pixel distortion.
 

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