CHAPTER 7

Question 1

Importance of Face Detection

Answer 1

The first step for any automatic face recognition system

First step in many Human Computer Interaction systems

Expression Recognition

Cognitive State/Emotional State Recognition

First step in many surveillance systems

Tracking: Face is a highly non rigid object

A step towards Automatic Target Recognition (ATR) or generic object detection/recognition

Video coding……

Question 2

T/F Face recognition, It works by identifying and measuring facial features in an image.

Answer 2

T

Question 3

*Facial recognition can identify human faces in ……………or ……………,

Answer 3

images , videos

Question 4

T/F Facial recognition can identify human faces in images or videos, determine if the face in two images belongs to the same person, or search for a face among a large collection of existing images.

Answer 4

T

Question 5

Skin detection

Answer 5

*Skin pixels have a distinctive range of colors
-Corresponds to region(s) in
RGB color space

*Skin classifier
-A pixel X = (R,G,B) is skin if it is in the skin (color) region

Question 6

PART 2 Skin detection

Answer 6

Learn the skin region from examples
* Manually label skin/non pixels in one or more “training images”
* Plot the training data in RGB space– skin pixels shown in orange, non-skin pixels shown in gray– some skin pixels may be outside the region, non-skin pixels inside.

Question 7

Skin classifier

Answer 7

Given X = (R,G,B): how to determine if it is skin or not?

• Nearest neighbor– find labeled pixel closest to X
• Find plane/curve that separates the two classes– popular approach: Support Vector Machines (SVM)
• Data modeling
  Face Recognition and Detection– fit a probability density/distribution model to each class

Question 8

Probabilistic skin classification

Answer 8

Question 9

Bayesian estimation

Answer 9

Goal is to choose the label (skin or ~skin) that maximizes the
posterior minimizes probability of misclassification

THIS IS CALLED : Maximum A Posteriori (MAP) estimation

Question 10

Choosing the Dimension K

Answer 10

How many eigenfaces to use?
Look at the decay of the eigenvalues
* the eigenvalue tells you the amount of variance “in the
direction” of that eigenface
* ignore eigenfaces with low variance

Question 11

Morphable Face Model

Answer 11

Use subspace to model elastic 2D or 3D shape variation (vertex
positions), in addition to appearance variation