1. Midterm Report “Makeup” Against Face Detection
Mentee: Tianying Zhou
Mentor: Vincent Bindschaedler
3/30/2017

2. Overview Recent progress
Face detection techniques are very popular nowadays, and a lot of APIs could achieve high accuracy.
However, what if a person doesn’t want to be recognized under the camera?
Understand how face detection models work(in this research, focus on Haar feature-based cascade Classifiers)
Perform visible adjustments on faces to get them misdetected
Overview

3. Techniques Python Environment Setup OpenCV
Virtual Machine on Linux Platform
Python
Essential Packages
OpenCV
Train my own Haar Cascade
Resize and convert images into grayscale
Add black blocks on different regions
Apply the face detection model and show results

4. Haar Features
Human faces share similar properties (Eyes darker than upper-cheeks)
Calculate the difference
Large # of features

5. Result Image

6. Next Goals Figure out thresholds of misdetection: size shape region
Further train my model with specific datasets
Train HOG model
Next Goals