Statistical approach to a color based f ace detection algorithm
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Statistical Approach to a Color-based F ace Detection Algorithm. EE 368 Digital Image Processing Group 15 Carmen Ng, Thomas Pun May 30, 2002. Statistical Approach to a Color-based Face Detection Algorithm. Assumptions 4 Stages: Pre-processing Skin Color Region Labeling

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Statistical Approach to a Color-based F ace Detection Algorithm

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Statistical approach to a color based f ace detection algorithm

Statistical Approach to a Color-based Face Detection Algorithm

EE 368

Digital Image Processing

Group 15

Carmen Ng, Thomas Pun

May 30, 2002


Statistical approach to a color based face detection algorithm

Statistical Approach to a Color-based Face Detection Algorithm

  • Assumptions

  • 4 Stages:

    • Pre-processing

    • Skin Color Region Labeling

    • Statistical Face Selection Techniques

    • Edge Detection

  • Advantages/Disadvantages


Statistical approach to a color based face detection algorithm1

Statistical Approach to a Color-based Face Detection Algorithm

Assumptions:

  • Color image

  • Multiple faces with similar area

  • Face orientation


Statistical approach to a color based face detection algorithm2

Statistical Approach to a Color-based Face Detection Algorithm

I . Image Pre-processing

  • Boundary extension

  • Improves accuracy


Statistical approach to a color based face detection algorithm3

Statistical Approach to a Color-based Face Detection Algorithm

II . Skin Color Region Labeling

  • Color-based

    • Chrominance extraction in YCbCr space

  • Morphological operations

    • Dilation and erosion


Statistical approach to a color based f ace detection algorithm

<= Original Image

Rough Mask =>


Statistical approach to a color based f ace detection algorithm

Binary Mask after Morphological Operations


Statistical approach to a color based face detection algorithm4

Statistical Approach to a Color-based Face Detection Algorithm

III . Statistical Analysis

  • Popular area finder

    • Facial feature detector (holes in binary images)

    • Popular area, width and height

  • Face rejection

    • Reject unpopular areas


Statistical approach to a color based f ace detection algorithm

Selected Face Regions after Stage III


Statistical approach to a color based face detection algorithm5

Statistical Approach to a Color-based Face Detection Algorithm

IV . Facial Feature (Eye) Detection

  • Approximate eye location

  • LPF to remove noise

  • Edge detection to locate strong edges


Statistical approach to a color based f ace detection algorithm

Typical Background

Typical Face

After LPF and Edge Detection


Statistical approach to a color based face detection algorithm6

Statistical Approach to a Color-based Face Detection Algorithm

Results/Conclusions:

  • 88% success rate

  • Adv: fast, no training required,work with video compression std.

  • DisAdv: min of faces required in image, work best with reliable facial detector


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