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
Previously : A robust, highly accurate method for detecting 20 facial points in images of
expressionless faces
BACKGROUND
A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
Inventor : under the name Harmonium by Paul Smolensky in 1986.Fast learning algorithms : mid-2000s by Geoffrey Hinton & collaborators.
RBMs have found applications in dimensionality reduction, classification, collaborative filtering, feature learning and topic modelling.
INTRODUCTIONOther methods : - track facial feature points independently or - build a shape model to capture the variations of face shape or - appearance regardless of the facial expressions and face poses
This method : capture the distinctions & variations of face shapes due to facial expression and pose change in a UNIFIED framework
CONTENT - MEDOTHOLOGY
1. Related work2. FrontalRBM & PoseRBM3. Facial feature tracking based on face shape prior
model 4. Experimental results
1. RELATED WORKFacial feature localization: 2 categories :
• Without shape prior models : track each facial feature point independently and ignore the prior knowledge about the face => sensitive with expression & pose
• With shape prior models : capture the dependence between facial feature points by explicitly modeling the general properties as well as the variations of facial shape or appearance
1. RELATED WORK
Facial feature localization:Recently methods:
• Active Shape Model (ASM) [2] and Active Appearance Model (AAM) : linear generative models
• Facial point detection using boosted regression and graph models : facial feature points are detected independently based on the response of the support vector regressor.
• Gaussian Process Latent Variable model : a single Gaussian is used for each facial component.
• Multi-State Facial Component Model of Tian and Cohn• ….
1. RELATED WORK
Restricted Boltzmann Machines based shape prior model:• Deep Belief Networks(DBNs)-like model : S. Eslami, N.
Heess, and J. Winn. (2012) - a strong model of object shape.
• Implicit mixture of Conditional Restricted Boltzmann Machines : G. Taylor, L. Sigal, D. Fleet, and G. Hinton (2010) - capture the human poses and motions (imRBM) under different activities such as walking, running etc
• …
CONTENT - MEDOTHOLOGY
1. Related work2. FrontalRBM & PoseRBM3. Facial feature tracking based on face shape prior
model 4. Experimental results
2. FRONTAL-RBM & POSE-RBM
the locations of facial feature points for frontal face when subjects show different facial expressions
the corresponding locations of facial feature points for non-frontal face under the same facial expression
H1 & H2 are two sets of hidden nodes
FACIAL FEATURE TRACKING BASED ON FACE SHAPE PRIOR MODEL
Gaussian assumption : estimate the prior probability by calculating the mean vector μp and covariance matrix Σp from the samples.
Kernel Density Function: to estimate the probability.
CONTENT - MEDOTHOLOGY
1. Related work2. FrontalRBM & PoseRBM3. Facial feature tracking based on face shape prior
model 4. Experimental results
RESULT
Experiments on synthetic data
FrontalRBM shows strong power as a face shape prior model.
RESULT
Experiments on MMI database: comparable to Facial point detection using boosted regression and graph models (rate error of 5.3 on 400 images ).
19 : Robust facial feature tracking under varying face pose and facial expression (Y. Tong, Y. Wang, Z. Zhu, and Q. Ji. - Nov 2007)
CONCLUSION• Improving The Accuracy And Robustness Of Facial Feature Tracking
Under Simultaneous Pose And Expression Variations
• 1st : A face shape prior model to capture the face shape patterns under
varying facial expressions for near-frontal face based on deep belief
networks
• 2nd : Extend the frontal face prior model by a 3-way RBM to capture face
shape patterns under simultaneous expression and pose variation.
• 3rd : Systematically combine the face prior models with image
measurements of facial feature points to perform facial feature point
tracking.
SWOT• STRENGTH ?Þ Experiments on many methods & do well comparing.
• WEAKNESS?
Þ The steps of the methods are not very clear.
Þ There is no specific correct detection rate.
• OPPORTUNITY ?
Þ Can be very useful for face detection related programs
• THREAT ?
Þ There are more than 6 basic expression.
ÞThe training data must be labelled manually.
OPINION
This paper has described a good method for face
detection under varying expression and specially with
occlusions.
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.
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