Dynamic Scalable Distributed Face Recognition System Security Framework

1. Dynamic Scalable Distributed Face Recognition System Security Framework by Konrad Rzeszutek B.S. University of New Orleans, 1999

2. Overview • Purpose • History of face recognition • Problems • Solution • Apollo • Components of Apollo • Face recognition technology used • Motion detection • Future work

3. Applications of face recognition • Surveillance Systems • Biomedical Systems (eye-replacement) • Military (anti-terrorist groups) • Security (logon authorization) • Autonomous vehicle navigation • … many more

4. History • Sir Francis Galton (1888) – Automatic method of classification of French prisoners. Called it mechanical selector. • Late 1960s started. • In 1980 research picked up dramatically. • Two branches of face recognition: • Geometric-features • Template matching

5. Geometric - profile • Profile features. • 8-100 control points • Six control points using B-spline • U.S. INS uses this one extensively.

6. Geometric - frontal • Frontal features • 8-16 features • Various distance from right and left eye to nose, nose to chin, eye to eye, etc. • Nose width, chin radii, eyebrow thickness, etc

7. Template matching • Face images are represented as vectors in an array (each image is identified as k) • Computations are carried on the model arrays resulting in hash values. • The matching image hash value is compared against the template images’ hash values.

8. Template matching, part 2 • The distance from the training images hash value determines the match. • Euclidian distance mostly used.

9. Principal Component Analysis • Turk and Pentland – Eigenface. • Most simplest – uses the whole image face as a template. • Variations of this use infrared images.

10. Template matching .. more • Isodensity line maps (brightness of image viewed as height of the mountain; isodensity lines corresponds to contour lines of equal altitude). • Neural network – eye and mouth regions feed into multi-layer perception engine that carries of the classification • .. Other are mostly various combinations of these two branches of face-recognition technologies.

11. Problems • Work done on a very selective set of face images, mostly: • In upright position • Lighting and background controlled • Either in frontal or profile view • Have no occlusions, facial hair • Most test cases are white males

12. Solution • A distributed system capable of handling large load of images, analyze them in near-real time, provide support for future enhancements and be scalable to the load. • Separates the functionality of a security system in three modules: recognition, notification, and replay.

13. Apollo

14. Components • Ares - the thin-client providing camera feed. • Hermes – the police officer directing traffic • Demeter – the storage for later replay of camera-feed • Nemesis – the face recognition engine • Mors – the notification event server

15. Ares • Passes the real-time camera feed through a motion-detection engine. • Transmits the feed to Nemesis for face-recognition and Demeter for storage. • Uses Jini/RMI to localize required components.

16. Hermes • Collects information about load of each components. • Is queried for its knowledge whenever a system in a pool requires another component. • Scalable – many of these system (Hermes) can coexist and provide the load information.

17. Demeter • Stores camera-feed for later replay and for storing the camera-feed on a archive media (WORM).

18. Nemesis • Face recognition module. Uses Eigenfaces technique to match images in near-real time. • Can be extended to use more algorithms and check image using many techniques. • If match found, an event is sent to Mors.

19. Mors • Receives events which notify about a possible face match. • Centralized pool where humans can visually check results and carry out proper procedures.

20. Face recognition - Nemesis • Eigenfaces – algorithm finds the PCA of faces, or eigenvectors of the covariance matrix. • “Each eigenvalue can be thought as an amount which, when subtracted from each diagonal matrix, makes the matrix singular. … Eigenvectors are characteristics vectors of the matrix” (from “Digital Image Processing by Castleman)

21. Eigenvalues and Eigenvectors • We are looking for  (eigenvectors) and  (eigenvalues) defined as: C =   Where C is our covariance matrix of the normalized face-vector =[1 2 … M ]

22. Weights • After the computation of eigenvalues and eigenvetors, we use M’ most significant eigenfaces (where each eigenface is the linear combination of eigenvalues and the face-image) to form a face subspace. • From the face subspace we calculate the weights (where T=[1 2 .. M’]): k= kT  k = 1,2, …, M’

23. Matching • We use the calculated weights to determine if the image is recognized. • Usually we use Euclidian distance.

24. Motion Detection • Motion detection is used on the client side – Ares. • It saves bandwidth and saves only frames that have content. • Algorithm uses two threshold functions: • The first is used to accommodate for possible artifacts introduced by the camera. • Second determines the if there is motion depending on the count of “clusters” of pixels that changed.

25. Motion Detection, .. more • Red is the “cluster” count.

26. Future work • Use more face recognition technologies so each can complement each other. • Expand framework to include other recognition technologies: iris, speech, etc. • Improve motion detection engine. • Face operations – automatically removing background. • Generate from one face a multitude of other faces with different alternations – bear (or lack of it), long hair, etc to expand possibly match.