Midterm Report “Makeup” Against Face Detection

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Presentation transcript:

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

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

Techniques Python Environment Setup OpenCV Virtual Machine on Linux Platform Python 2.7.13 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

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

Result Image

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