2. What is Face Detection & Recognition??
The difference between face detection and recognition is that in detection
we just need to determine if there is some face in the image, but in
recognition we want to determine whose face it is.
3. Face Detection
In Face Detection, we need to be able to define the general
structure of a face.
5. Motivation
Automatic age and gender
classification has become relevant
to an increasing amount of
applications, particularly since the
rise of social platforms and social
media. Nevertheless, performance
of existing methods on real-world
images is still significantly lacking,
especially when compared to the
tremendous leaps in performance
recently reported for the related
task of face recognition.
6. Problem
Age and Gender Classification using Facial
Features. A human can easily make these estimates
from faces. Yet, it is still a challenging task for a
computer.
Even just with facial images, humans would use
some high-level information, like the skin tone, the
hairstyle, the facial hair. That can be hard for the
prototype system to extract. On the other hand the
system will use computer vision techniques that
humans are not able to use.
7. Approach
{ 'Full_Name': 'Eryn Olson',
'Gender': 'Female',
'Recommended_Ids': ['erynolson', 'eryn-olson-62639510a',
'eryn-olson-432679b1', 'eryn-olson-1351aa74', 'courtney-
tillman-8b62a664', 'chance-cozby-13235224', 'bill-gates-
b1a606b0', 'sarah-eves-2937a719', 'andrew-solheim-
810a3517', 'andrea-cundiff-76020535', 'mollie-harper-
6b850947', 'tyler-shaw-934b7', 'donna-conroy-66987340',
'paul-wood-73455920'],
'User_ID': 'eryn-olson-50328143',
'age': 20 }
Scrap LinkedIn Profiles
Get recommended ids list
Find Age and predict gender
Store all images and labelize them
8. Baseline classifier
Feature Extraction
o Face detection from picture
o Edge Detection
Logistic Regression
o Basic Classification method
o Predicts binary response
11. CNN or ConvNet
A Convolutional Neural Network (CNN) is comprised of one or more convolutional layers and then
followed by one or more fully connected layers as in a standard multilayer neural network.
16. Conclusion / Future Work
Keep collecting more data to get better accuracy.
Try to diminish testing error and remove noise from
data.
Use PCA for feature reduction
Use more age group, and predict the age.
