A short quiz on humans (mostly faces)

Adrian F. Clark

What are Haar features?
- the number of corners in a region
- measures of the number of edges in a region
- the sum of differences between a pair of images
- differences between the sums of pixels in rectangular regions
Haar features are simple, which makes them quick to calculate.
Faces are allegedly more attractive if:
- facial features are slightly asymmetrical
- the lips are oval in shape
- the hair and skin are in the same region in HSV space
- 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.
Why is the integral image representation used in Viola-Jones?
- it stores where faces lie in an image
- it stores the sum of all the pixels in an image
- it allows Haar features to be calculated in constant time
- it allows features to be concatenated into a feature vector
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.
The Viola-Jones face detection algorithm is based around the use of which type of features?
- ORB features
- SIFT features
- Edges detected using Canny's edge detector
- 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 is covariance?
- a measure of the similarity of variations in the images
- a pair of images having similar variances
- a pair of images having dissimilar 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?
- Support Vector Machine
- Convolutional Neural Network
- Linear Discriminant Analysis
- Principal Component Analysis
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.
How is Principal Component Analysis performed?
- by Eigen decomposition of the variance of an image
- by Linear Discriminant Analysis of the covariance matrix
- by Eigen decomposition of the covariance matrix
- by Linear Discriminant Analysis of the variance of an image
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 is Adaptive Boosting?
- a method for combining multiple weak classifiers into one strong classifier
- a method for improving the performance of a single weak classifier
- an adaptive way of choosing the best single classifier for a task
- 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
What is Affective Computing?
- a way of performing computer vision that is more accurate
- a way of recognising facial expressions
- a face recognition algorithm
- taking into account the emotional state of the user
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 does Principal Component Analysis (PCA), as used in eigenfaces, do?
- finds the directions in feature space with the largest variations
- finds the most important direction in an image
- finds the most similar set of features in a database
- splits up features into components
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.