Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers

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

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


1. Face Detection




METHODOLOGY

METHODOLOGY


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


ANALYSIS THE IRISES

=> Locate the irises (by vertical and horizontal histograms:

comparing rows & columns pixels) => x & y coordinate.

=> Rotate the images if necessary

METHODOLOGY


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.


1. Face Detection




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.


1. Face Detection




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).


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.


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.

RESULT

Testing on 300 images

Average rate : 93%

RESULT

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.

THANK YOU

Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers

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