fully automatic facial feature point detection using gabor feature based boosted classifiers
TRANSCRIPT
JAMES COOK AUSTRALIA INSTITUTE OF HIGHER LEARNING
IN SINGAPORE
HEALTH DIAGNOSTIC BY ANALYSING FACE IMAGES USING MOBILE DEVICES
Instructor : Dr. Insu Song
Student : Ho Thi Hoang YenEmail: [email protected]
INTRODUCTION
A robust, highly accurate method for detecting 20 facial points in images of expressionless faces
INTRODUCTION
Facial feature points are generally referred to as facial salient points such as :1. the corners of the eyes (ABFG)2. corners of the eyebrows (ED)3. corners and outer mid points
of the lips (IKJL)4. corners of the nostrils (H)5. tip of the nose (N)6. the tip of the chin (M)
How do they localization facial feature points ?
Þ Currently, localization of facial points is usually carried out by manually
labeling the required set of points.
Previous methods categories:
1. Texture-based Methods: modeling local texture around a given feature point.
2. Shape-based Methods: regard all facial feature points as a shape.
=> None of them detects all 20 facial feature points and None has high
accuracy.
INTRODUCTION
INTRODUCTIONFace detection using Haar feature based GentleBoost classifier
feature extraction based on Gabor filtering
d) feature selection and classification using GentleBoost classifier,
output of the system compared to the face drawing with facial landmark points we aim to detect
The Method Consists Of 4 Steps:
1. Face Detection
2. Region Of Interest (ROI) Detection
3. Feature Extraction
4. Feature Classification.
METHODOLOGY
METHODOLOGY
1. FACE DETECTION
=> To build a system capable of automatically labeling facial feature
points in a face image, it is first necessary to localize the face in the image.
=> Using robust real-time face detection of Viola-Jones (replace
adaboost with gentleboost)
Results :
- Training data : 5000 faces and millions of non-face patches from the web
- Detecting test : 422 images from the Cohn-Kanade database => 100%
detection rate
The Method Consists Of 4 Steps:
1. Face Detection
2. Region Of Interest (ROI) Detection
3. Feature Extraction
4. Feature Classification.
METHODOLOGY
METHODOLOGY
2. Region Of Interest (ROI) Detection
Divide into 2 part : iris part and mouth part.
1. Divide the face region horizontally into two parts 2. The upper face region is again divided into two halves in a vertical direction so that each eye can be analyzed separately.
METHODOLOGY
2. Region Of Interest (ROI) Detection
ANALYSIS THE IRISES
=> Locate the irises (by vertical and horizontal histograms:
comparing rows & columns pixels) => x & y coordinate.
=> Rotate the images if necessary
METHODOLOGY
2. Region Of Interest (ROI) Detection
ANALYSIS THE MOUTH
Þ With those distance, we can locate the medial point of mouth.
Þ Test on Cohn-Kanade database: 99% correct rate. (2 cases fail)
Þ Use Iris and medial points to divide the face into 20 regions for further calculations.
The Method Consists Of 4 Steps:
1. Face Detection
2. Region Of Interest (ROI) Detection
3. Feature Extraction
4. Feature Classification.
METHODOLOGY
METHODOLOGY 3. Feature Extraction
Divided the face into 20 ROIs, each ROIs corresponds to a facial point
to be detected. Gabor wavelet method is used to do this extraction. A slide
window 13x13 size slid to the ROIs and get the facial point for extraction.
Feature vector for each facial point is extracted from the 13×13 pixels image patch centered on that point
f0 is the central frequency of a sinusoidal plane wave, θ is the anti-clock wise rotation of the Gaussian and the plane wave, and α and β are the parameters for scaling two axis of the elliptic Gaussian envelope.
The Method Consists Of 4 Steps:
1. Face Detection
2. Region Of Interest (ROI) Detection
3. Feature Extraction
4. Feature Classification.
METHODOLOGY
METHODOLOGY 4. Feature Classification.
- GentleBoost feature templates are learned using a representative set
of positive and negative examples.
- The size of a example point (feature vector) is 8281 size matrix.
- In contrast to Adaboost, Gentleboost uses real valued features Þ Faster .
Þ Perform better for objects detection problems.
Þ Numerically robust .
Þ Outperform other boosting algorithms (PCA , FLD , LFA).
METHODOLOGY 4. Feature Classification.
Positive and negative examples for training point The big white square on the inner corner of the eye represents 9 positive examples. Around that square are 8 negative examples randomly chosen near the positive examples. Another 8 negative examples are randomly chosen from the rest of the region.
METHODOLOGY 4. Feature Classification.
There is a 25×8281 size matrix representing training data for each ROI for each training image. (9 pos + 16 neg)
In the testing phase, each ROI is filtered first by the same set of Gabor filters used in the training phase (in total, 48 Gabor filters are used).
For each position of the sliding window, GentleBoost classifier outputs a response depicting the similarity between the 49-dimensional representation of the sliding window compared to the learned feature point model.
RESULT
Training set
The facial feature detection method was trained and
tested on the Cohn-Kanade database, which consists
of approximately 2000 gray-scale image sequences in
nearly frontal view from over 200 subjects, male and
female, being 18 to 50 years old. => but authors used only
first 300 frames for training and testing.
CONCLUSION
• A robust, highly accurate method for fully automatic
detection of 20 facial feature points in images of
expressionless faces
• Using Gabor feature based boosted classifiers.
• When tested on images from the Cohn-Kanade database,
with possible in-plane head rotations and recorded under
various illumination conditions, the method has achieved
average recognition rates of 93%.
SWOT• STRENGTH ?Þ Robust, high accuracy, automatically detection of 20 feature points, not
sensitive with head-pose & illumination
• WEAKNESS?
Þ Not very effective with expressional faces ( 93% rate based on 300 public
expressionless samples of CK database ) => authors didn’t guarantee that the
method’s performance reported below will remain the same
• OPPORTUNITY ?
Þ Can be very useful for face detection related programs
• THREAT ?
Þ Results with public images (not from Cohn-Kanade) may not as effected.
OPINION
This paper has described very clearly the algorithm
for face detection.
It is valuable for all kinds of researches related to
face, to the system for interacting between human &
computer , or the face recognition and to FACE
ANALYSIS FOR HEALTH PURPOSE.