A short quiz on humans (mostly faces)

Adrian F. Clark

What is Affective Computing?
- a face recognition algorithm
- taking into account the emotional state of the user
- a way of performing computer vision that is more accurate
- a way of recognising facial expressions
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 the basis of the HOG feature detector?
- a histogram of pixel values
- a histogram of edge gradients
- a histogram of edge orientations
- a histogram of colour differences
HOG stands for "histogram of oriented gradients", so the histogram is of edge orientations, also known as edge directions.
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.
What does Principal Component Analysis (PCA), as used in eigenfaces, do?
- finds the most similar set of features in a database
- splits up features into components
- finds the most important direction in an image
- finds the directions in feature space with the largest variations
PCA finds the direction in feature space with the largest variance, then the next-largest direction that is perpendicular to the first -- and so on. For the mathematically-inclined, this forms a set of basis vectors to the feature space using the criterion of maximising variance.
The Viola-Jones face detection algorithm is based around the use of which type of features?
- ORB features
- Edges detected using Canny's edge detector
- SIFT features
- Haar 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.
What are Haar features?
- the sum of differences between a pair of images
- differences between the sums of pixels in rectangular regions
- the number of corners in a region
- measures of the number of edges in a region
Haar features are simple, which makes them quick to calculate.
Why is the integral image representation used in Viola-Jones?
- 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
- it stores where faces lie 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 Adaptive Boosting?
- a method for improving the performance of a single weak classifier
- a way of improving the contrast of an image based on the local grey levels
- a method for combining multiple weak classifiers into one strong classifier
- an adaptive way of choosing the best single classifier for a task
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
What is covariance?
- a measure of the similarity of variations in the images
- a pair of images having dissimilar variances
- a pair of images having similar variances
- another name for cross-correlation
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 $\sum (P_{ij} - \bar P) (Q_{ij} - \bar Q)$.
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.