1. Face Components detection
Joel Kamdem Teto

2. OVERVIEW
Objectives
Methodology
Dataset
Demo

3. Objectives Detect Faces Detect eyes Detect Nose Detect Mouth
Optimal Prediction of gender and age.
Multi-activity android application and UX

4. Methodologie
HAARCASCADE FILES are Trained templates of more than 3500 positive samples that contains the researched features.
HAARCASCADE FILES
Mouth
Eyes
Face
Nose

5. Methodology Gender and age classification
Linear model trained on Matlab
Trained for multiple different parameters to obtain the highest accuracy with Linear Model

6. Methodology Multi-activity application
Created an activity for each part of the app ( simple detect, components and gender classification)
Reduce the amount of resources required at the same time
Utilization of intents to move from the main screen to other parts of the app

7. Dataset wiki5.csv Results: 3209 samples of Labeled data
Labels include : gender and age
2000 samples for training data and the other 1209 for testing purpose
Linear model applied : result = A * b ( where b is the parameter we are training )
Results:
77.50 % of accuracy with gender
63.19 % of accuracy with age( provided an error of +/- 5 years)

8. Difficulties
Finding a suitable training data to apply my first methodology
Understanding the parameters of the detectMultiscale openCV function
Understanding the different type of search patterns, when in come to search based on a classifier.
Finding the optimal parameters for each component detection
Improving the UX
Documentation
TIME

9. Demo
Thank you