1. F ACE D ETECTION FOR A CCESS C ONTROL By Dmitri De Klerk Supervisor: James Connan

2. I NTRODUCTION 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 detection No need to position your face inside a fixed size box.

3. U SER R EQUIREMENTS Expected from a solution: Detect a persons face within an image. frontal face pose minor variations in lighting conditions minor variations in facial expressions Not expected from a solution: non frontal face pose extreme lighting conditions: darkness or too much light total or partial occlusion of face

4. R EQUIREMENT A NALYSIS 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.

5. A NALYSIS & I NTERFACE

6. D ESIGN & I MPLEMENTATION Face Detection Viola and Jones approach Adaboost Haar Features Cascade classifiers Face Recognition Neural network [Desmond Van Wyk 2006]

7. D ETECTOR 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)

8. P ERFORMANCE T ESTING MIT+CMU frontal face test set. Images collected at CMU and MIT. 275 Correctly Detected out of 472 images. 58% Detection rate

9. T OOLS AND L ANGUAGES Java Media Framework (JMF) Capturing images and communicating with camera NetBeans IDE Java Programming Languages

10. F ACE DETECTION DEMO

11. R EFERENCES 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/sid ebar.html http://www.cognotics.com/opencv/servo_2007_series/part_2/sid ebar.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]

12. Q UESTIONS AND A NSWERS Thank you!