FACE DETECTION FOR

ACCESS CONTROLBy Dmitri De Klerk

Supervisor: James Connan

INTRODUCTION Project description

Implementation of a face detection system that may be used for access control.

Face detection (done prior to face recognition) Finds the locations and sizes of human faces in

arbitrary (digital) images.

Motivation for face detectionNo need to position your face inside a fixed

size box.

USER REQUIREMENTS

Expected from a solution:Detect a persons face within an image. frontal face poseminor variations in lighting conditionsminor variations in facial expressions

Not expected from a solution:non frontal face poseextreme lighting conditions:

darkness or too much lighttotal or partial occlusion of face

REQUIREMENT ANALYSIS

Proposed Solution Detect faces in image. Pass the biggest detection to the recognition

face. User closest to the camera.

Resize the users face to the standard recognizable size.

Recognize the users face Check whether the user is authorized.

Grant or deny the user access.

ANALYSIS & INTERFACE

DESIGN & IMPLEMENTATION

Face Detection Viola and Jones approach Adaboost Haar Features Cascade classifiers

Face Recognition Neural network [Desmond Van Wyk 2006]

DETECTOR

Training Data 3398 hand labeled faces

All frontal Took 7 days to train

607 non faces different scale Faces are normalized

Scale, translation

Many variations Across individuals Illumination Pose (rotation both in plane and out)

PERFORMANCE TESTING

MIT+CMU frontal face test set. Images collected at CMU and MIT.

275 Correctly Detected out of 472 images. 58% Detection rate

TOOLS AND LANGUAGES

Java Media Framework (JMF) Capturing images and communicating with

camera

NetBeans IDE Java Programming Languages

FACE DETECTION DEMO

REFERENCES AdaBoost

http://en.wikipedia.org/wiki/AdaBoost

Builds on the work of Desmond Van Wyk Face Recognition System for Access Control (2006)

How face detection work http://www.cognotics.com/opencv/servo_2007_series/part_2

/sidebar.html

Fast Face Detection Using AdaBoost Julien Meynet [16 July 2003]

The Boosting Approach to Machine Learning – An overview Robert E. Schapire [19 December 2001]

QUESTIONS AND ANSWERS Thank you!