Face Components detection Joel Kamdem Teto

OVERVIEW Objectives Methodology Dataset Demo

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

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

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

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

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)

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

Demo Thank you