Face Detection Using DSP Processor TMS320DM6437Presented by Nikam Ganesh Kacharu under the guidance of Dr. R. S. Holambe
Department of Instrumentation Engineering, S.G.G.S. Institute of Engineering and Technology, Nanded-431 606
January 3, 2013Nikam Ganesh Guide: Dr. R.S.Holambe Face Detection using DSP Processor 1/54
Content
Introduction to DSP Processor Color Space Model Face Detection Algorithms Implementation Future Work Conclusion
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TMS320DM6437 DSP Processor Kit
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Features of TMS320DM6437
Operating up to 600MHz 1 TVP5146 video decoder 4 video DAC outputs Embedded JTAG emulation interface I2C interface with onboard EEPROM Power supply-5v,1.2v,3.3v,1.8v AIC33 stereo codecs 16MB of Non-volatile ash memory,64MB Nand Flash
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Requirements of TMS320DM6437
Hardware Requirement :Digital CCD camera DSP Processor TMS320DM6437 EVM kit Composite Video Cable Television
Software Requirement :Code Composer Studio version 3.3 DM6437 EVM Driver and Target Content Flash Burn DSK Image/Video Processing Library(IMGLIB) JTAG Emulator
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Block Diagram of EVM TMS320DM6437
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Flow Diagram
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TVP5146M2 Video decoder Feature
Ten video input terminals can be congured to a combination of RGB,YPbPr,CVBS or S-Video video input Accepts NTSC, PAL, composite video, S-video and RGB Four ADC of each 10 bit Its output is in YCbCr format Low power and Small package
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VPSS Block Diagram
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VPFE Driver
CCD Controller :Supports upto 75-MHz sensor clocks Accepting raw image/video data from sensor CCD camera through video decoder TVP5146M2 Supports YCbCr 4:2:2 format Supports progressive and interlaced sensors
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VPBE Driver
On Screen Display :Provides 2 video and 2 OSD windows simultaneously Separate control for each window Having 2x and 4x zoom in both direction Display Priority : Rect. cursor OSDWIN1 OSDWIN0 VIDWIN1 VIDWIN0 Background color
Video Encoder :Converts display frame from OSD into desired (NTSC D1) output signal
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Block Diagram of Face Recognition System
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Y-Cb-Cr Color Space Model
The Color space is only format of representing Color,Brightness,Luminance,Saturation Chroma component of image is more important Every Pixel contains the Y-Cb-Cr information in the format 4:2:2
Y
Cb
Y
Cr
Y
Cb
Y
Cr
Y .....
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Face Detection Algorithm
Different Algorithm available :1 2 3 4
Haar Feature Based Face Detection Skin color Based Face Detection Eye blinking pattern detection Appearance, face and movement detection
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Haar Feature Based Face Detection Algorithm
Capture Input Frame Resize it and Convert it into Gray Make it Integral Image Load Haar-Cascade.mat File Scaling operation to slide a window Initialize the Array to store Object Detected
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Haar Feature Based Object Detection Algorithm
Maximum of search scale iterationCall One scale Detection function to nd the object in image for current scale We are checking through 21 Stages If calculated feature value is less than threshold then it is face region otherwise non-face region like this we get object detected
If Object is detected we get left side top (x,y)co-ordinate and width and height, otherwise empty matrix. Highlight face area with rectangular box. crop face area from image and save to database.
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Flow Chart
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Flow Chart
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Haar Features UsedCalculate Feature value: A + D B C s(x, y) = s(x, y 1) + i(x, y) ii(x, y) = ii(x 1, y) + s(x, y)
(1) (2)
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Two Rectangular Feature
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Concept of Integral Image
ii(x, y ) =x