CS201: Computer Vision Lect 06: Face Detection John Magee Slides - - PowerPoint PPT Presentation

CS201: Computer Vision Lect 06: Face Detection John Magee

Slides Courtesy of Diane H. Theriault

Framing

• In Computer Vision, we want to analyze and

interpret images and video

• First:

– How are images represented? – How are images formed? – What kinds of tasks are we interested in doing?

Framing

• Next:

How do you find things you are looking for?

– Version 1: Thresholding / Color Analysis

• Post-processing (morphological operators)

– Need to clean up messy output

• Binary image analysis

– Try to reason about the objects that you find

Framing

• Next:

How do you find things you are looking for?

– Version 2: Compute features in the images

• Example: Gradients
• Example: General Filter Responses (Today)
• Example: Corners and Keypoints (Next week)

– How to combine and use image features will be

• ur focus when considering object recognition

and many other tasks

Framing

• Different points of view about tasks in

computer vision. Examples:

– Algorithmic (e.g. represent image as a graph) – Statistical (Images are noisy measurements) – Signal processing (think of image as a 2D signal) – Machine Learning (train models using data)

Question of the Day:

• How can we find faces in images?

Face Detection

• Compute features in the image (Today)
• Apply a classifier
• Viola & Jones. “Rapid Object Detection using a

Boosted Cascade of Simple Features”

Features with Image Filtering

• Perform image filtering by

convolving an image with a “filter”/”mask” / “kernel” to

• btain a “result” / “response”
• The value of the result will be

positive in regions of the image that “look like” the filter

• One type of image feature is

the way the image responds to different types of filters

Image Filter

Image Filtering

• To perform convolution:
• Multiply each element of the

filter with the corresponding element of the image

• Sum the results
• 1
• 1

1 1

Image Filter

• 1
• 1

1 1

What is the response of the image to the filter (the result) in the region denoted by the red box?

Image Filtering

• 1
• 1

1 1

Image Filter

1 1

• 1
• 1
• To perform convolution:
• Multiply each element of the

filter with the corresponding element of the image

• Sum the results

What is the response of the image to the filter (the result) in the region denoted by the red box?

Image Filtering

• 1
• 1

1 1

Image Filter

1

• 1
• 1

1

• To perform convolution:
• Multiply each element of the

filter with the corresponding element of the image

• Sum the results

What is the response of the image to the filter (the result) in the region denoted by the red box?

Image Filtering

• 1
• 1

1 1

Image Filter

1

• 1

1

• 1

1

• 1

1

• 1

Filter

• To perform convolution:
• Multiply each element of the

filter with the corresponding element of the image

• Sum the results

What is the response of the image to the filters (the result) in the region denoted by the red box?

Features with Image Filtering

• Perform image filtering by

convolving an image with a “filter”/”mask” / “kernel” to

• btain a “result” / “response”
• The value of the result will be

positive in regions of the image that “look like” the filter

• One type of image feature is

the way the image responds to different types of filters

Image Filter

What do Faces “Look Like”?

• Make a “face filter”?

What do Faces “Look Like”?

• Chosen features are responses of the image to

the “Haar-like” box filters

Image With Faces

Filter Responses

Filter Responses

Image with Non-faces

Filter Responses

Filter Responses

Convolution is Expensive!

• Computational complexity of brute force

convolution is linear in the number of pixels in the filter

– if your image is NxM, and your filter is 3x3, then the cost is 9*N*M (that’s a teeny face!) – If your image is NxM and your filter is 20 x 20, then the cost is 400*N*M

Computing the Responses Efficiently

• Viola and Jones chose “box” filters
• To compute the response, you

take the difference of the sum of the image values in the boxes (Red minus blue)

• What if you could compute the

sum of the image values in a box without visiting every pixel in the box?

Box filter

Computing the Responses Efficiently

• The Integral Image is the

computational trick that made this paper a star

• In the Integral Image, every pixel

contains the sum of all of the pixels above and to the left

Computing the Responses Efficiently

• Once integral image has been

computed, the sum of the pixels in any sized box can be computed with 4 numbers

• Red

Computing the Responses Efficiently

• Once integral image has been

computed, the sum of the pixels in any sized box can be computed with 4 numbers

• Red – blue

Computing the Responses Efficiently

• Once integral image has been

computed, the sum of the pixels in any sized box can be computed with 4 numbers

• Red – blue – green

Computing the Responses Efficiently

• Once integral image has been

computed, the sum of the pixels in any sized box can be computed with 4 numbers

• Red – blue – green + orange
• (lower right) – (upper right) –

(lower left) + (upper left)

Discussion Questions:

• Describe how you use the integral image to compute the sum
• f any region in an image
• Using the integral image, how many operations does it take to

compute the sum of a region that is 3×3? 10×10? 10×30?

• How would you use the integral image to efficiently compute

the response of an image region to a box filter?

• How many operations do you need to compute the response
• f an image region to a box filter containing two pieces?

three? four?

• What is a simple way you might try to classify image regions

as containing a face or not, using the response of the image region to a box filter?