A short quiz on humans (mostly faces)

Adrian F. Clark

Why is the integral image representation used in Viola-Jones?
- it stores where faces lie in an image
- it allows Haar features to be calculated in constant time
- it allows features to be concatenated into a feature vector
- it stores the sum of all the pixels in an image
The main attraction the integral image representation is that it allows Haar features of any size to be calculated in constant time, using the same underlying mathematics as the anti-aliased texture mapping on a typical graphics card.
What is Affective Computing?
- taking into account the emotional state of the user
- a way of recognising facial expressions
- a way of performing computer vision that is more accurate
- a face recognition algorithm
Affective Computing attempts to take account of the emotional state of the user. Much of the research into affective computing directed towards identifying the expression on a user's face, but there are other techniques for doing this (skin conductivity, ECG, etc) and the subject also covers things that can be done when this state is known.
What is Adaptive Boosting?
- an adaptive way of choosing the best single classifier for a task
- a method for improving the performance of a single weak classifier
- a method for combining multiple weak classifiers into one strong classifier
- a way of improving the contrast of an image based on the local grey levels
Adaptive booting is the way that the Viola-Jones algorithm combines multiple Haar features, each of which is a weak classifier individually, into a strong classifier
How is Principal Component Analysis performed?
- by Linear Discriminant Analysis of the covariance matrix
- by Eigen decomposition of the variance of an image
- by Linear Discriminant Analysis of the variance of an image
- by Eigen decomposition of the covariance matrix
PCA is performed by calculating the covatiance matrix and then performing an eigen decomposition of it. The resulting eigenvectors or Principal Components describe the different ways in which the images vary and can be used for "transforming" an image. When that is done, the eigenvalues are a set of weights that describe how the principal components are combined to give that image. These weights can be used for recognition: the closer they are to something in the training image set, the more similar they are.
Faces are allegedly more attractive if:
- the hair and skin are in the same region in HSV space
- facial features are slightly asymmetrical
- the lips are oval in shape
- the shape matches the golden ratio
People are supposed to have more beautiful faces if the shape is closer to the golden ratio. No, I don't believe it either.
What is covariance?
- a pair of images having similar variances
- a measure of the similarity of variations in the images
- another name for cross-correlation
- a pair of images having dissimilar variances
The covariance measures whether variations in images happen "in step". For two images $P$ and $Q$ with means $\bar P$ and $\bar Q$, it is $\sum (P_{ij} - \bar P) (Q_{ij} - \bar Q)$.
What are Haar features?
- differences between the sums of pixels in rectangular regions
- the number of corners in a region
- the sum of differences between a pair of images
- measures of the number of edges in a region
Haar features are simple, which makes them quick to calculate.
The Viola-Jones face detection algorithm is based around the use of which type of features?
- Haar features
- Edges detected using Canny's edge detector
- SIFT features
- ORB features
Viola-Jones is based around Haar features, which can be calculated very quickly. They provide "weak classifiers" of faces, and carefully-chosen ones can be assembled into a "cascade" that works well. The algorithm is probably implemented on your mobile phone.
Which of the following techniques is "eigenfaces" built on?
- Linear Discriminant Analysis
- Principal Component Analysis
- Support Vector Machine
- Convolutional Neural Network
Eigenfaces is based on a Principal Component Analysis or Eigen decomposition of the training data. The other methods listed are also machine learning techniques and some of them may be used in conjunction with PCA to recognise faces, but the resulting technique is not eigenfaces.
What is the basis of the HOG feature detector?
- a histogram of edge gradients
- a histogram of colour differences
- a histogram of edge orientations
- a histogram of pixel values
HOG stands for "histogram of oriented gradients", so the histogram is of edge orientations, also known as edge directions.