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Face Detection for Access Control

Face Detection for Access Control. By 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)

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Face Detection for Access Control

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  1. Face Detection for Access Control By Dmitri De Klerk Supervisor: James Connan

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

  3. User Requirements • 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. 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.

  5. Analysis & Interface

  6. Design & Implementation • Face Detection • Viola and Jones approach • Adaboost • Haar Features • Cascade classifiers • Face Recognition • Neural network [Desmond Van Wyk2006]

  7. 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)

  8. Performance Testing • MIT+CMU frontal face test set. • Images collected at CMU and MIT. • 275 Correctly Detected out of 472 images. • 58% Detection rate

  9. Tools and Languages • Java Media Framework (JMF) • Capturing images and communicating with camera • NetBeans IDE • Java Programming Languages

  10. Face detection demo

  11. 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 • JulienMeynet [16 July 2003] • The Boosting Approach to Machine Learning – An overview • Robert E. Schapire [19 December 2001]

  12. Questions and Answers Thank you!

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