Face Recognition Summary –Single pose –Multiple pose –Principal components analysis...
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Transcript of Face Recognition Summary –Single pose –Multiple pose –Principal components analysis...
Face Recognition
• Summary– Single pose– Multiple pose– Principal components analysis– Model-based recognition– Neural Networks
Single Pose
• Standard head-and-shoulders view with uniform background
• Easy to find face within image
Aligning Images
Alignment– Faces in the training set must be aligned with
each other to remove the effects of translation, scale, rotation etc.
– It is easy to find the position of the eyes and mouth and then shift and resize images so that are aligned with each other
Nearest Neighbour
• Once the images have been aligned you can simply search for the member of the training set which is nearest to the test image.
• There are a number of measures of distance including Euclidean distance, and the cross-correlation
Principal Components
• PCA reduces the number of dimensions and so the memory requirement is much reduced.
• The search time is also reduced
Two ways to apply PCA (1)
• We could apply PCA to the whole training set.
• Then each face is represented by a point in the PC space
• We could then apply nearest neighbour to these points
Two ways to apply PCA (2)
• Alternatively we could apply PCA to the set of faces belonging to each person in the training set
• Each class (person) is then reprented by a different ellipsoid and Mahalanobis distance can be used to classify a new unknown face
• You need a lot of images of each person to do this
Problems with PCA
• The same person may sometimes appear differently due to– Beards, moustaches– Glasses,– Makeup
• These have to be represented by different ellipsoids
-------(2)--------------(3)--------------(4)-------
-------(5)--------------(6)--------------(7)-------
-------(8)--------------(9)--------------(10)-------
Problems with PCA
• Facial expressions– Differing facial expressions
• Opening and closing the mouth• Raised eyebrows• Widening the eyes• Smiling, frowing etc,
• These mean that the class is no longer ellipsoidal and must be represented by a manifold
Facial Expressions
• There are six types of facial expression• We could use PCA on the eyes and mouth – so we
could have eigeneyes and eigenmouths
Anger Fear Disgust Happy Sad Surprise
Multiple Poses
• Heads must now be aligned in 3D world space
• Classes now form trajectories in feature space
• It becomes difficult to recognise faces because the variation due to pose is greater than the variation between people
Model-based Recognition
• We can fit a model directly to the face image
• Model consists of a mesh which is matched to facial features such as the eyes, nose, mouth and edges of the face.
• We use PCA to describe the parameters of the model rather than the pixels.
Model-based Recognition
• The model copes better with multiple poses and changes in facial expression.