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RGB Level Face Detection. Jian Zhang Miao He Jing Chen May.27th,2002. How to find faces?. Mission Analysis. lots of faces in the images, time-consuming to search the face candidates, some faces got overlapped

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RGB Level Face Detection

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Rgb level face detection

RGB Level Face Detection

Jian Zhang

Miao He

Jing Chen


How to find faces

How to find faces?

Mission analysis

Mission Analysis

  • lots of faces in the images, time-consuming to search the face candidates, some faces got overlapped

  • sidelight instead of diffused light. Influence from shade, complexion, rotation of face, and the inhomogeneity of background

  • 1280*960 high resolution image, slows down the detection speed and more noise

2 step algorithm

2-step Algorithm

  • 1. Skin Toner Masks

  • 2. Support Vector Machine (SVM)

Skin toner masks 1

Skin Toner Masks (1)

  • Mask based on hue in HSV space

Skin toner masks 2

Skin Toner Masks (2)

  • Mask based on RGB statistics

Skin toner masks 3

Skin Toner Masks (3)

  • Mask for red color

Skin toner masks 4

Skin Toner Masks (4)

  • Mask to remove the ground

Skin toner masks 5

Skin Toner Masks (5)

  • Remove small regions

Skin toner masks 6 final mask

Skin Toner Masks (6)—Final Mask

Svm theory 1

SVM Theory(1)

  • Given the training sample {(xi,di)}, i=1…L, find the Lagrange multipliers {αi}, i=1…L, that maximize the objective function

  • Subject to the constraints


    2) for i=1,2…L

    Where C is a user-specified positive parameter.

Svm theory 2

SVM Theory(2)

  • The discriminate is

Pre processing


  • In the tradeoff between speed and accuracy, we choose 12-by-12 and 4 gray scale samples.

  • Eliminating the effect of side light.

  • Histogram equalization

Rgb level face detection

Face Samples in RGB,4,3,2 Gray Scale

Training a svm

Training a SVM

  • a large date set, about 1000 face (different scales and positions) and more than 7000 non-face samples.

  • takes more than 30 hours on the ISL lab computer to get one set of training result.

  • From now on,SVM shows its advantage

Teaching and learning process 1

Teaching and Learning Process(1)

Teaching and learning process 2

Teaching and Learning Process(2)

Our improvement

Our improvement

  • Add rotated face into face data set. Thus we can detect these special faces

  • faces concentrate against non-faces

    B is an observation constant. Thusenhance the detection accuracy.

Testing results 1

Testing Results (1)

  • Now we need merge the detection points to give the final decision. Model the three-scale detection as a diversity situation like in wireless communication channels.

Testing results 2

Testing Results (2)

  • maximum-ratio diversity

  • perform dilation to binary image

Final detection

Final Detection

Further implementation

Further Implementation



  • The authors want to express out thankfulness to Prof. Girod’s excellent instruction. We all learn a lot from this interesting course.

  • We would also like to thank our teaching assistant, Chuo-ling Chang, for his patience and help.

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