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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.
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
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]