Statistical Approach to a Color-based Face Detection Algorithm

EE 368

Digital Image Processing

Group 15

Carmen Ng, Thomas Pun

May 30, 2002

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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

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Statistical Approach to a Color-based Face Detection Algorithm

Assumptions: Color image Multiple faces with similar area Face orientation

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Statistical Approach to a Color-based Face Detection Algorithm

I . Image Pre-processing Boundary extension Improves accuracy

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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

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Rough Mask =>

<= Original Image

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Binary Mask after Morphological Operations

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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

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Selected Face Regions after Stage III

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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

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Typical Background

Typical Face After LPF and Edge Detection

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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