Using a sparse learning Relevance Vector Machine in Facial Expression Recognition
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Transcript of Using a sparse learning Relevance Vector Machine in Facial Expression Recognition
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Using a sparse learning Relevance VectorMachine in Facial Expression Recognition
Dragos Datcu
April 14, 2006
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Introduction
The utility of facial expression recognition technology:
Human computer interfaces
Safety, surveillance- terorist identification- safe driving, somnolence detection- access control
Psychology
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Problem definition
How to realize a fully automatic facial expression recognition system
using a sparse learning Relevance Vector Machine?
The automatic facial expression recognition system includes:
- face detector- facial feature extractor for mouth, left and right eye- Facial Characteristic Point - FCP extractor- facial expression recognizer
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Facial expression recognition system
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I. Face detection
The method makes use of:
- Viola&Jones features (24x24 size samples, 162336 features/sample)- Evolutionary AdaBoost (150 size population)
- Relevance Vector Machine (RVM) - weak classifier
- training data set includes: 4916 faces and 10000 non-faces.
The detector consists in a 32 layer cascade of classifiers using 4297 features
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Viola&Jones features
The basic types:
Applied on an image:
The value is: Haar value =P
PixelintensitydarkareasP
Pixelintensitylightareas
For a 24x24 image, there exist more than 160.000 such features.
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AdaBoost algorithm
Discrete AdaBoost [Freund and Schapire (1996b)]
1. Start with weights wi = 1/N, i = 1,...,N.
2. Repeat for m = 1, 2,...,M:
(a) Fit the classifier fm(c) {1, 1} using weights wi on the training data.(b) Compute errm = Ew[1(y=fm(x))], cm = log((1 errm)/errm).(c) Set wi wiexp[cm1(yi=fm(xi))], i = 1, 2,...,N, and renormalize so thatP
i wi = 1.
3. Output the classifier sign[PM
m=1 cmfm(x)].
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The RVM-based weak classifier
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Evolutionary AdaBoost
E.A. performs an efficient search for the representative Viola&Jonesfeatures for classification
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Face detection
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Face detection
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Face detection
Cascaded classifier with T layers
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Face detection
Example: choosing the propper weak classifier for three different V&Jfeatures.2-fold cross validation results on three week classifiers for face detection based on Haar-like
features
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Face detection
ROC curves of three kernels, obtained by adjusting each classifiers threshold
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Face detection results
RVM test results, both training and testing are performed on MIT CBCL database
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Face detection results
RVM test results, the training is done using MIT CBCL, the testing is done on CMU
database
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II. Facial feature extraction
The facial features to be extracted are: left/right eye and mouth areas.
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III. FCP model
Kobayashi and Hara model the face through 30 FCPs
There are three steps involved in the FCP detection:
1. FCP detection using corner detectors2.a. FCP detection using RVM classifier
2.b. FCP detection using integral projection method
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III.1. FCP detection using corner detectors
There are two corner detectors that are used as a first stage for FCP detection.
The hybrid corner detector stands for a combination of two corner detectors:
- Harris
- Sojka
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III.2.a. The FCPs to be extracted with RVM based E.A.
classifier
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III.2.a. The stages of training the FCP detector
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III.2.a. FCP data set
BioID dataset and the Carnegie Melon dataset
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III.2.a. EA characteristics
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III.2.a. FCP detection results
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III.2.b. FCP detection, integral projection method
It is used to extract the rest of the points.
- projects the image into the vertical and horizontal axes
- obtain the boundaries
- the boundaries of the features have relatively high contrast
- the image is presented by two 1D orthogonal projection functions: IP Fv
, IP Fh
,
M I P F v, M I P F h, V IP Fv, V IP Fh, GP Fv, GP Fh
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