Health diagnostic - Reasech Proposal
Transcript of Health diagnostic - Reasech Proposal
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]
CONTENT
1. Background
2. Introduction
3. Problems & Solution
4. Preparing DATA
5. Results & Conclusion
6. Future Plan
7. References
FACIAL PALSY
Facial Palsy is the paralysis of nerve VII Þ Recover after 6
months
BUTÞ This is also the
symptom of a
STROKE – which causes many deaths in the world.
STROKE
STROKE: - The third leading
cause of death.- Each year:
approximate 795000 people suffer a stroke
- More than 140,000 people die in the United States.
TOO LATE!
SKIN CANCER & ROSACEA
APPSTORE & GOOGLE PLAY
INTRODUCTION
PROBLEMSServer side :
Build a symptoms
data warehouse
Client side : Take face
image
Analysis the photo if there
is any symptom (server or
client)BUT, there is still a long way from concept to application, we have to face many problems, example:- How fast the analysis can be with a huge database while
the database becomes bigger and bigger by time?- Where should the database locate? On the cloud and
receive the request from the client then process OR on the client side and need to be updated all the time?
- Will people agree to share their personal sensitive information as photos and health condition?
FACIAL POINTS EXTRACTIONFully Automatic Facial Feature Point Detection Using Gabor
Feature Based Boosted Classifiers. (Danijela & Maja)
• Using robust real-time face detection of Viola-Jones (replace adaboost with gentleboost) (shape based method)
• Using the position of iris to divide the face’s into Regions of Interested (ROIs)
• Feature extraction by Gabor WaveletResult :
• Face detection : 100%• Feature points detection : 93%• Outperformed PCA , FLD and LFA
FACIAL POINTS EXTRACTIONFully Automatic Facial Feature Point Detection Using
Gabor Feature Based Boosted Classifiers.
Weakness:- Cannot guarantee on Expressional
faces
FACIAL POINTS EXTRACTIONFacial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
(Wu , Wang & Ji) • Capture the distinctions & variations of face shapes due
to facial expression and pose change in a UNIFIED framework
• Using FrontalRBM & PoseRBM • Using 3-way RBM : capture the relationship between
frontal & non-frontal faces.Result• Improve the accuracy and robustness for various
face pose & expressions
DATA COLECTING
RESULT
• Find the solution to the problem:Extract facial feature points
Handle expressional & expressionless faces data by merging
Gabor Filter Method and Restricted Boltzmann Machines.
• Propose a plan to collect trustable data to fulfill the training
task.
CONCLUSION
• Facial Palsy, Skin Cancer, Flu, Stroke,.. can be soon
diagnostic.
• By doing literature review, we proposed a method to build
health diagnostic systems.
• Apply improved Jones-Viola method & Restricted Boltmann
Machines method to detect faces and extract facial
features.
• Grouping the data by diseases and applying to do searching
match sickness with input face data.
FUTURE PLAN
• Implement the two methods into a mobile application (on
iOS/Android)
• Implement the server system (stored sample data)
• Identify problems and search for solutions.
• Do more research on algorithms to improve the performance
of the program.
• Improve data
• Test and release.
REFERENCES
• http://www.facialparalysisinstitute.com/photo-gallery.html
• http://nccd.cdc.gov
• Vukadinovic, D., & Pantic, M. (2005). fully automatic facial feature point
detection using gabor feature based boosted classifiers. systems, man
and cybernetics, 2005 IEEE international conference on. IEEE.
• Wu, Y., Wang, Z., & Ji, Q. (2013). Facial feature tracking under varying
facial expressions and face poses based on restricted boltzmann
machines. Computer Vision and Pattern Recognition (CVPR), 2013
IEEE Conference on. IEEE.
THANK YOUQuestions ?