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# RGB Level Face Detection PowerPoint PPT Presentation

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

RGB Level Face Detection

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## RGB Level Face Detection

Jian Zhang

Miao He

Jing Chen

May.27th,2002

### 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. Support Vector Machine (SVM)

• Mask based on hue in HSV space

• Mask based on RGB statistics

• Mask to remove the ground

• Remove small regions

### 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

1)

2) for i=1,2…L

Where C is a user-specified positive parameter.

### 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

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

### 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

### 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)

• 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)

• maximum-ratio diversity

• perform dilation to binary image

## Final Detection

### Acknowledgement

• 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.