face detection and neural networks n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Face Detection and Neural Networks PowerPoint Presentation
Download Presentation
Face Detection and Neural Networks

Loading in 2 Seconds...

play fullscreen
1 / 14

Face Detection and Neural Networks - PowerPoint PPT Presentation


  • 135 Views
  • Uploaded on

Face Detection and Neural Networks. Todd Wittman Math 8600: Image Analysis Prof. Jackie Shen December 2001. Face Detection. Problem: Given a color image, determine if the image contains a human face. That is, can you tell our governor from a toaster?. vs.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Face Detection and Neural Networks' - lotus


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
face detection and neural networks

Face DetectionandNeural Networks

Todd Wittman

Math 8600: Image Analysis

Prof. Jackie Shen

December 2001

face detection
Face Detection

Problem: Given a color image, determine if the image contains a human face.

That is, can you tell our governor from a toaster?

vs.

Answer: The picture on the right contains a human face. I think.

Applications: AI, tracking, automated security, video retrieval

overview of face detection methods
Overview of Face Detection Methods
  • Edge detection to recognize features and spatial relationships (Marsicoi, ‘97).
  • HSV-space segmentation and vector angular-based distance measure (Andoutsos, ‘99).
  • Chroma chart to detect skin tones and edge detection to identify eyes and mouth (Cai, ‘99).
  • Unsupervised Adaptive Skin Color Model, also called clustering (Bergasa, ‘99).
neural network
Neural Network

Goal: Given a set of inputs X and desired outputs T, determine the weights s.t. X generates T.

Idea: Similar inputs will give similar outputs.

X

T

Hidden

Layer

Training: Set weights to minimize .

Levenberg-Marquad Algorithm (multi-dim steepest descent).

Training is very expensive computationally. If there are x input

nodes, t output nodes, and p hidden nodes, then # weights = (x+t)p.

face detection nn
Face Detection NN

Input: Color image.

Output: P(w|x) = probability that image contains a face. (Only 1 output node.)

Set 1 for face, 0 for no face.

P=0

P=1

  • 3 Possible Outputs
    • P > 0.5 FACE
    • P < 0.5 NOT FACE
    • P = 0.5 DON’T KNOW
1st attempt interpolated image
1st Attempt: Interpolated Image

Input X: The pixel values of the image at N selected grid points.

Original Interpolated Output

P=1

Since each pixel has three values (RGB), our input

vector X will have length 3N.

I tried a small case: N=25.

The network took over an hour to train for the

training set on the next slide.

results
Results

P values for 20 images in training set.

P=0.5 for all training images.

The interpolated images

can’t be interpreted.

2nd attempt rgb histograms
2nd Attempt: RGB Histograms

Input X: The 3 histograms of the RGB values, appended as 1 vector.

Each histogram has N=20 bins.

So size of input vector is 3N=60.

Idea: Neural network will pick out the frequency of flesh tones.

results1
Results

After 100 iterations (1 hour, 1241 weights), the Levenberg-Marquad algorithm was able to correctly classify all 20 training images.

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

But on a test set of 13 images, got 7 correct (53.8%).

3rd attempt yes histograms
3rd Attempt: YES Histograms

RGB histograms were too similar.

Y = 0.253R + 0.684G + 0.063B

E = 0.5R - 0.5G

S = 0.25R + 0.25G - 0.5B

RGB

YES

Input X: 3 YES histograms appended as one vector.

results2
Results

After training for 100 iterations, 3 images in training set were mis-classified.

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

- 0.1

But on test set, correctly identified 13 out of 13 images (100%).

th th that s all folks
Th-th-that’s All, Folks!

You can try my Matlab code:

www.math.umn.edu/~wittman/faces/main.html

slide14

Input

Layer

Output

Layer

Hidden

Layer

T

X