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CSE 2100
Software Development Project 1

             Project Title
          Face Detection
Supervisor

      Mr. Md. Aminul Haque Akhand
               Assistant Professor
Department of Computer Science and Engineering
 Khulna University of Engineering and Technology



        Credit: Abu Saleh Md. Musa (0907013)
                Sanjoy Dutta (0907008)
Objectives

• The objective of our project is to design
  software that can detect human faces from an
  image.
What is Face Detection
• Face detection is a computer
  technology that determines the
  locations and sizes of human
  faces in arbitrary (digital)
  images. It detects facial
  features and ignores anything
  else, such as buildings, trees
  and bodies.
Why we chose Face Detection
            Project?

• Compatible with Modern Era.
• Not common in JAVA.
• Basic programme for Recognition(Recognition
  is not possible without Detection).
• Security Maintenance and Media
  Empowering.
• Needed for visual applications in Robotics.
Examples of Implementation 1
      Picasa Photo Viewer
          (people panel)
Examples of Implementation 2

•              Facebook Tagging
Procedure at a Glance
• Read an image from disk (.JPG, etc.)
• Convert it into a jjil.core.Image
• Generally we’ll have an RGB image (colored image) and
  so need to convert it to 8-bit grayscale, which is what
  the Gray8DetectHaarMultiScale class requires.
• Load facial properties to the class form Haar profile for
  detecting faces.
• Apply Gray8DetectHaarMultiScale to our 8-bit grey
  image.
• Retrieve result from Gray8DetectHaarMultiScale.
Step by Step Analysis

                            Step 1
As part of preprocessing we ensured certain things to make our
   software functional:
• The input is a colored image
• There are multiple faces with frontal view and upright orientation
• The size of faces within the image should approximately be the
   same
• Little deviation in brightness for all the faces within the image
• Faces have to be greater than a certain size in the image so that
   facial features can be detected.
• Standard dimension is not more than 1600 X 1200 px.
Step 2
• Convert image to jjil.core.Image
  Where jjil means Jon's Java Imaging
  Library.
Step 3




Main Image    R/G/B Image   8 bit grayscale
Step 4
Applying Gray8DetectHaarMultiScale to
  our 8-bit grey image.
Final Output
Limititions

• It can’t detect faces without frontal view
  and upright orientation
Future Plan


• Design an intelligence system that can analyse
  objects.
• Make them enable to see and feel like us.
• Remove all its limitations and eager to develop this
  software.
• Enable them to suggest us to make the best use of
  objects.
• Empower media and security services.
Conclusion
•



    Thanks all

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Project Face Detection

  • 1. CSE 2100 Software Development Project 1 Project Title Face Detection
  • 2. Supervisor Mr. Md. Aminul Haque Akhand Assistant Professor Department of Computer Science and Engineering Khulna University of Engineering and Technology Credit: Abu Saleh Md. Musa (0907013) Sanjoy Dutta (0907008)
  • 3. Objectives • The objective of our project is to design software that can detect human faces from an image.
  • 4. What is Face Detection • Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies.
  • 5. Why we chose Face Detection Project? • Compatible with Modern Era. • Not common in JAVA. • Basic programme for Recognition(Recognition is not possible without Detection). • Security Maintenance and Media Empowering. • Needed for visual applications in Robotics.
  • 6. Examples of Implementation 1 Picasa Photo Viewer (people panel)
  • 7. Examples of Implementation 2 • Facebook Tagging
  • 8. Procedure at a Glance • Read an image from disk (.JPG, etc.) • Convert it into a jjil.core.Image • Generally we’ll have an RGB image (colored image) and so need to convert it to 8-bit grayscale, which is what the Gray8DetectHaarMultiScale class requires. • Load facial properties to the class form Haar profile for detecting faces. • Apply Gray8DetectHaarMultiScale to our 8-bit grey image. • Retrieve result from Gray8DetectHaarMultiScale.
  • 9. Step by Step Analysis Step 1 As part of preprocessing we ensured certain things to make our software functional: • The input is a colored image • There are multiple faces with frontal view and upright orientation • The size of faces within the image should approximately be the same • Little deviation in brightness for all the faces within the image • Faces have to be greater than a certain size in the image so that facial features can be detected. • Standard dimension is not more than 1600 X 1200 px.
  • 10. Step 2 • Convert image to jjil.core.Image Where jjil means Jon's Java Imaging Library.
  • 11. Step 3 Main Image R/G/B Image 8 bit grayscale
  • 12. Step 4 Applying Gray8DetectHaarMultiScale to our 8-bit grey image.
  • 14. Limititions • It can’t detect faces without frontal view and upright orientation
  • 15. Future Plan • Design an intelligence system that can analyse objects. • Make them enable to see and feel like us. • Remove all its limitations and eager to develop this software. • Enable them to suggest us to make the best use of objects. • Empower media and security services.
  • 16. Conclusion • Thanks all